diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index 9828f2e12..5b8851a89 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -27,7 +27,7 @@ jobs:
steps:
- name: Checkout
- uses: actions/checkout@v3
+ uses: actions/checkout@v4
- name: Initialize CodeQL
uses: github/codeql-action/init@v2
diff --git a/.github/workflows/coverage.yml b/.github/workflows/coverage.yml
index b7bdabb5e..c997076ba 100644
--- a/.github/workflows/coverage.yml
+++ b/.github/workflows/coverage.yml
@@ -13,7 +13,7 @@ jobs:
run:
runs-on: ubuntu-latest
steps:
- - uses: actions/checkout@v3
+ - uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Setup Python
diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
index e91ae94a6..610c499f1 100644
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -13,7 +13,7 @@ jobs:
name: Build wheel
runs-on: ubuntu-latest
steps:
- - uses: actions/checkout@v3
+ - uses: actions/checkout@v4
with:
fetch-depth: 0
- uses: actions/setup-python@v4
@@ -34,7 +34,7 @@ jobs:
name: Build src distribution
runs-on: ubuntu-latest
steps:
- - uses: actions/checkout@v3
+ - uses: actions/checkout@v4
with:
fetch-depth: 0
- uses: actions/setup-python@v4
diff --git a/.github/workflows/mypy.yml b/.github/workflows/mypy.yml
index 2fb845d1a..d2984e8f9 100644
--- a/.github/workflows/mypy.yml
+++ b/.github/workflows/mypy.yml
@@ -16,7 +16,7 @@ jobs:
runs-on: ubuntu-latest
name: Run my[py] linter
steps:
- - uses: actions/checkout@v3
+ - uses: actions/checkout@v4
- uses: actions/setup-python@v4
with:
python-version: "3.10"
diff --git a/.gitignore b/.gitignore
index 8e3cb04d9..d54b195e2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,7 @@
model_*.zip
+*.qasm
+*.DS_Store
+*-checkpoint.ipynb
/build*
/cmake-build-*
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index e1453135a..0314af9a4 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -87,9 +87,12 @@ repos:
files: ^(src|tests|setup.py)
args: []
additional_dependencies:
+ - pytket-qiskit
+ - qiskit_optimization
+ - qiskit_nature
+ - qiskit_finance
- importlib_resources
- pytest
- types-setuptools
- types-requests
- types-tqdm
- - pytket-qiskit
diff --git a/README.md b/README.md
index 691a643d7..945a78c34 100644
--- a/README.md
+++ b/README.md
@@ -10,20 +10,75 @@
-# MQT Predictor: Automatic Prediction of Good Compilation Paths
+# MQT Predictor: Automatic Device Selection with Device-Specific Circuit Compilation for Quantum Computing
-MQT Predictor is a framework suggesting a compilation options to use for an arbitrary quantum circuit according to the user's needs.
-To this end, we provide two models for predicting good compilation options and returning the accordingly compiled quantum circuit.
+MQT Predictor is a framework that allows one to automatically select a suitable quantum device for a particular application and provides an optimized compiler for the selected device.
+It not only supports end-users in navigating the vast landscape of choices, it also allows to mix-and-match compiler passes from various tools to create optimized compilers that transcend the individual tools.
+Evaluations on more than 500 quantum circuits and seven devices have shown that—compared to Qiskit's and TKET's most optimized compilation flows—the MQT Predictor yields circuits with an expected fidelity that is on par with the best possible result that could be achieved by trying out all combinations of devices and compilers and even achieves a similar performance when considering the critical depth as an alternative figure of merit.
-## Supervised Machine Learning Model (referred to as "ML")
+Therefore, MQT Predictor tackles this problem from two angles:
-Here, the problem is treated as a statistical classification task and a supervised machine learning model is trained to predict
-the best compilation options for a given quantum circuits consisting of a qubit technology, a quantum device, a respective compiler, and its settings:
+1. It provides a method (based on Reinforcement Learning) that produces device-specific quantum circuit compilers by combining compilation passes from various compiler tools and learning optimized sequences of those passes with respect to a customizable figure of merit). This mix-and-match of compiler passes from various tools allows one to eliminate vendor locks and to create optimized compilers that transcend the individual tools.
-
+2. It provides a prediction method (based on Supervised Machine Learning) that, without performing any compilation, automatically predicts the most suitable device for a given application. This completely eliminates the manual and laborious task of determining a suitable target device and guides end-users through the vast landscape of choices without the need for quantum computing expertise.
-Furthermore, the resulting methodology does not only provide end-users with a prediction on the best compilation options,
-but additionally provides insights on why certain decisions have been made—allowing them to learn from the predicted results.
+
+
+ 
+
+
+
+In the following, we provide a brief overview of the two approaches.
+
+## Device-specific Quantum Circuit Compiler Using Reinforcement Learning Model
+
+Compilation, fortunately, is not new per-se, since classical compilers have seen a similar trend of an increasing complexity and variety in the past.
+To not reinvent the wheel and make use of the decades of classical compiler optimization, quantum compilation is modeled in a similar fashion and classical reinforcement learning is used to predict compilation pass sequences optimizing for the chosen figure of merit.
+
+Through distinct constraints and a unifying interface, the framework supports the combination of techniques
+from different compilers and optimization tools in a single compilation flow.
+The compilation process is modelled as a Markov Decision Process and takes three inputs:
+
+1. Training circuits
+2. The targeted quantum device
+3. The figure of merit to optimize for
+
+
+
+ 
+
+
+
+The trained reinforcement learning model then acts as a compiler and can be used to compile any quantum circuit for the targeted device.
+
+In this implementation, compilation passes from both IBM's Qiskit and Quantinuum's TKET are utilized for the RL training
+of the optimized compiler.
+We trained one RL model for each currently supported quantum device:
+
+- OQC Lucy with 8 qubits
+- IonQ Harmony with 11 qubits
+- IonQ Aria1 with 25 qubits
+- IBM Montreal with 27 qubits
+- Quantinuum H2 with 32 qubits
+- Rigetti Aspen-M2 with 80 qubits
+- IBM Washington with 127 qubits
+
+## Automatic Device Selection Using Supervised Machine Learning
+
+A naive approach to select the best quantum device for a given quantum circuit would be to compile it for all devices, e.g., using the trained RL models which act as specialized compilers for supported quantum devices.
+Afterwards, the resulting compiled circuits must be evaluated according to some figure of merit to identify the most promising device.
+However, doing this for each and every to-be-compiled quantum circuit is practically infeasible since compilation is a time-consuming task.
+
+The MQT Predictor learns from previous compilations of other quantum circuits and models the problem of determining the most promising device for a circuit and figure of merit as a statistical classification task—a task well suited for supervised machine learning.
+For that, the framework is trained with a set of quantum circuits and their respective compilation options for all supported devices for a given figure of merit:
+
+
+
+ 
+
+
+
+The trained model then acts as a predictor and can be used to predict the most suitable device for a given quantum circuit and figure of merit.
For evaluation of our methodology, seven supervised machine learning classifiers have been used:
@@ -37,29 +92,21 @@ For evaluation of our methodology, seven supervised machine learning classifiers
In our exemplary scenario, the Random Forest classifier achieved the best performance.
-This ML model comprises three main functionalities:
+# The MQT Predictor framework: Automatic device selection and optimized compilation
-- The pre-trained Random Forest classifier to easily predict compilation options for an unseen quantum circuit
- in real-time and compile for the respective prediction,
-- all other trained algorithms, and
-- the possibility to adjust and customize the whole training data generation process, e.g., to add training data, compilation options, or adapt the evaluation function.
+From a user's perspective, the framework is used as follows:
-## Reinforcement Learning Model (referred to as "RL")
-
-In this work, we take advantage of decades of classical compiler optimization and propose a
-reinforcement learning framework for developing optimized quantum circuit compilation flows.
-Through distinct constraints and a unifying interface, the framework supports the combination of techniques
-from different compilers and optimization tools in a single compilation flow.
-The compilation process is modelled as a Markov Decision Process:
-
-
+
+
+ 
+
+
-In this implementation, compilation passes from both IBM's Qiskit and Quantinuum's TKET are utilized for the RL training
-of the optimized compiler.
-We trained one RL model for each of the three optimization criteria of expected fidelity, minimal critical depth, and
-maximal parallelism.
+Any uncompiled quantum circuit can be provided together with the desired figure of merit.
+The framework then automatically predicts the most suitable device for the given circuit and figure of merit and compiles the circuit for the predicted device.
+The compiled circuit is returned together with the compilation information and the selected device.
-# Usage of MQT Predictor
+# Usage
First, the package must be installed:
@@ -70,16 +117,14 @@ First, the package must be installed:
Now a prediction can be made for any `qiskit.QuantumCircuit` object or `qasm` file:
```python
-from mqt.predictor import ml, rl
+from mqt.predictor import qcompile
-compiled_qc_ML, compilation_info_ML = ml.qcompile("qasm_file_path_or_QuantumCircuit")
-compiled_qc_RL, compilation_info_RL = rl.qcompile(
- "qasm_file_path_or_QuantumCircuit", opt_objective="fidelity"
+compiled_qc, compilation_info, selected_device = qcompile(
+ "qasm_file_path_or_QuantumCircuit", figure_of_merit="expected_fidelity"
)
```
-In the RL model, the `opt_objective` options are `fidelity`, `critical_depth`, `gate_ratio`,
-and `mix` (combination of `fidelity` and `critical_depth`).
+Currently available figures of merit are `fidelity` and `critical_depth`.
# Examination of all seven trained classifiers of the ML model
@@ -89,15 +134,6 @@ To play around with all the examined models, please use the `notebooks/ml/evalua
The adjustment of the following parts is possible:
-### Compilation Path and Compilation Pipelines
-
-Definition of the to be considered compilation options for
-
-- chosen qubit technologies,
-- their respective devices,
-- the suitable compilers, and
-- their compilation settings.
-
### Evaluation Metric
To make predictions which compilation options are the best ones for a given quantum circuits, a goodness definition is needed.
@@ -116,15 +152,19 @@ After the adjustment is finished, the following methods need to be called to gen
from mqt.predictor import ml
predictor = ml.Predictor()
-predictor.generate_compiled_circuits()
-res = predictor.generate_trainingdata_from_qasm_files()
-ml.helper.save_training_data(res)
+predictor.generate_compiled_circuits(figure_of_merit="expected_fidelity")
+training_data, name_list, scores_list = predictor.generate_trainingdata_from_qasm_files(
+ figure_of_merit="expected_fidelity"
+)
+ml.helper.save_training_data(
+ training_data, name_list, scores_list, figure_of_merit="expected_fidelity"
+)
```
Now, the Random Forest classifier can be trained:
```python
-predictor.train_random_forest_classifier()
+predictor.train_random_forest_classifier(figure_of_merit="expected_fidelity")
```
Additionally, the raw training data may be extracted and can be used for any machine learning model:
@@ -139,7 +179,9 @@ Additionally, the raw training data may be extracted and can be used for any mac
indices_test,
names_list,
scores_list,
-) = predictor.get_prepared_training_data(save_non_zero_indices=True)
+) = predictor.get_prepared_training_data(
+ save_non_zero_indices=True, figure_of_merit="expected_fidelity"
+)
```
# Repository Structure
@@ -147,10 +189,9 @@ Additionally, the raw training data may be extracted and can be used for any mac
```
.
├── notebooks/
-│ ├── ml/
-│ │ ├── ...
-│ └── rl/
-│ └── ...
+│ ├── evaluations/
+│ │ ├── ...
+│ └── example.ipynb
├── src/
│ ├── mqt/
│ └── predictor/
@@ -169,7 +210,7 @@ Additionally, the raw training data may be extracted and can be used for any mac
# References
-In case you are using MQT Predictor with the ML model in your work, we would be thankful if you referred to it by citing the following publication:
+The MQT Predictor is based on the following publications:
```bibtex
@INPROCEEDINGS{quetschlich2023prediction,
@@ -183,8 +224,6 @@ In case you are using MQT Predictor with the ML model in your work, we would be
which is also available on arXiv:
[](https://arxiv.org/abs/2210.08027)
-In case you are using the RL model in your work, we would be thankful if you referred to it by citing the following publication:
-
```bibtex
@INPROCEEDINGS{quetschlich2023compileroptimization,
author = {N. Quetschlich and L. Burgholzer and R. Wille},
diff --git a/img/ml.png b/img/ml.png
new file mode 100644
index 000000000..93fbe2890
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diff --git a/img/mqt_predictor.png b/img/mqt_predictor.png
new file mode 100644
index 000000000..6b02f3f52
Binary files /dev/null and b/img/mqt_predictor.png differ
diff --git a/img/mqt_predictor_ml.png b/img/mqt_predictor_ml.png
deleted file mode 100644
index be0c2a6ef..000000000
Binary files a/img/mqt_predictor_ml.png and /dev/null differ
diff --git a/img/mqt_predictor_rl.png b/img/mqt_predictor_rl.png
deleted file mode 100644
index e31d451d4..000000000
Binary files a/img/mqt_predictor_rl.png and /dev/null differ
diff --git a/img/rl.png b/img/rl.png
new file mode 100644
index 000000000..dae3b7111
Binary files /dev/null and b/img/rl.png differ
diff --git a/notebooks/evalutions/overall_evaluation.ipynb b/notebooks/evalutions/overall_evaluation.ipynb
new file mode 100644
index 000000000..1f940bc49
--- /dev/null
+++ b/notebooks/evalutions/overall_evaluation.ipynb
@@ -0,0 +1,541 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "697e4c2b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import numpy as np\n",
+ "from mqt.predictor import rl, ml\n",
+ "df = pd.read_csv(ml.helper.get_path_results(), sep=',')\n",
+ "\n",
+ "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
+ "plt.rcParams[\"font.size\"] = 30\n",
+ "\n",
+ "apply_normalization = False"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5cd42294",
+ "metadata": {},
+ "source": [
+ "# 2x2 Matrix With Mean Results and Optimization Criterion Comparison"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6a887a2c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tmp_df = df[df[\"MQTPredictor_expected_fidelity_expected_fidelity\"]>=0]\n",
+ "tmp_df = tmp_df[tmp_df[\"MQTPredictor_critical_depth_expected_fidelity\"]>=0]\n",
+ "MQT_expected_fidelity = [tmp_df[\"MQTPredictor_expected_fidelity_expected_fidelity\"].mean(), tmp_df[\"MQTPredictor_expected_fidelity_critical_depth\"].mean()]\n",
+ "MQT_critical_depth = [tmp_df[\"MQTPredictor_critical_depth_expected_fidelity\"].mean(), tmp_df[\"MQTPredictor_critical_depth_critical_depth\"].mean()]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c8d299e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(np.round(MQT_expected_fidelity,2))\n",
+ "print(np.round(MQT_critical_depth,2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf93d18b-b61a-47ae-988c-590e25e2f203",
+ "metadata": {},
+ "source": [
+ "## Top 3 Bins"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e649aff4-7fb8-457f-9ec8-c1cd97bbc4ea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def bins_labels(bins, **kwargs):\n",
+ " bin_w = (max(bins) - min(bins)) / (len(bins) - 1)\n",
+ " plt.xticks(np.arange(min(bins)+bin_w/2, max(bins), bin_w), bins, **kwargs)\n",
+ " plt.xlim(bins[0], bins[-1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7efb6e96-22c3-4739-b480-75d7b9a42785",
+ "metadata": {},
+ "source": [
+ "## Calcualte Data Points"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "76d28b11-cf2b-4286-a09a-05098b0cfd84",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def calculate_median(row, column_name):\n",
+ " columns = [col for col in row.index if col.endswith(column_name) and not \"MQT\" in col]\n",
+ " values = [value for col, value in row[columns].items() if value != -1]\n",
+ " return np.median(values) if values else None\n",
+ "\n",
+ "df['expected_expected_fidelity_median'] = df.apply(calculate_median, column_name=\"_expected_fidelity\",axis=1)\n",
+ "df['critical_depth_median'] = df.apply(calculate_median, column_name=\"_critical_depth\",axis=1)\n",
+ "df['expected_expected_fidelity_min_other'] = df.apply(lambda row: row[[col for col in row.index if col.endswith('_expected_fidelity') and 'MQT' not in col]].replace(-1, np.nan).min(skipna=True), axis=1)\n",
+ "df['expected_expected_fidelity_max_other'] = df.apply(lambda row: row[[col for col in row.index if col.endswith('_expected_fidelity') and 'MQT' not in col]].max(), axis=1)\n",
+ "df['critical_depth_min_other'] = df.apply(lambda row: row[[col for col in row.index if col.endswith('_critical_depth') and 'MQT' not in col]].replace(-1, np.nan).min(skipna=True), axis=1)\n",
+ "df['critical_depth_max_other'] = df.apply(lambda row: row[[col for col in row.index if col.endswith('_critical_depth') and 'MQT' not in col]].max(), axis=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "15623d11-8024-4d65-ac23-b468af76ab70",
+ "metadata": {},
+ "source": [
+ "## Optionally: Normalize"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "86906f27-0c80-4f63-a8fe-c1310a38e106",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if apply_normalization:\n",
+ " divider_column_name = 'expected_expected_fidelity_max_other'\n",
+ " \n",
+ " # Get the list of column names ending with \"_expected_fidelity\"\n",
+ " columns_to_divide = [col for col in df.columns if col.endswith(\"_expected_fidelity\") or col in ['expected_expected_fidelity_min_other', 'exptected_expected_fidelity_max_other', 'exptected_expected_fidelity_median' ]]\n",
+ " \n",
+ " # Iterate through each column and perform the division\n",
+ " for col_name in columns_to_divide:\n",
+ " df[col_name] = df[col_name].divide(df[divider_column_name])\n",
+ " \n",
+ " divider_column_name = 'critical_depth_max_other'\n",
+ " \n",
+ " # Get the list of column names ending with \"_expected_fidelity\"\n",
+ " columns_to_divide = [col for col in df.columns if col.endswith(\"_critical_depth\") or col in ['critical_depth_min_other', 'critical_depth_max_other', 'critical_depth_median' ]]\n",
+ " \n",
+ " # Iterate through each column and perform the division\n",
+ " for col_name in columns_to_divide:\n",
+ " df[col_name] = df[col_name].divide(df[divider_column_name])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "686119db-4ea4-4d61-9fd4-50d06131301c",
+ "metadata": {},
+ "source": [
+ "# Expected Fidelity"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "221a7376-c5e1-483d-ba5d-aa476ed55da0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "predictor = ml.Predictor()\n",
+ "\n",
+ "training_data = predictor.get_prepared_training_data(figure_of_merit=\"expected_fidelity\", save_non_zero_indices=True)\n",
+ "indices_test = training_data.indices_test\n",
+ "names_list = training_data.names_list\n",
+ "\n",
+ "test_benchmarks_expected_fidelity = [names_list[index_test] for index_test in indices_test]\n",
+ "df_filtered_expected_fidelity = df[df[\"file_path\"].isin(test_benchmarks_expected_fidelity)]\n",
+ "df_filtered_expected_fidelity = df_filtered_expected_fidelity[df_filtered_expected_fidelity[\"MQTPredictor_expected_fidelity_expected_fidelity\"]>=0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "87e9f7d1-f565-4d14-ad75-b0e8bb2820fe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"Fid Improvement: \", df_filtered_expected_fidelity.loc[df['MQTPredictor_expected_fidelity_expected_fidelity'] != np.inf, 'MQTPredictor_expected_fidelity_expected_fidelity'].max())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1a83daed-b560-405f-867d-e2b568773b39",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "len(df_filtered_expected_fidelity)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "03a3ad43-c57f-45fe-b0bc-e0476e504178",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_filtered_expected_fidelity['MQTPredictor_expected_fidelity_expected_fidelity'].describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "74cea6af-5e3d-4350-9e81-004a96cf697e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plot_kind = \"line\"\n",
+ "df_filtered_and_sorted_expected_fidelity = df_filtered_expected_fidelity.sort_values(by=['MQTPredictor_expected_fidelity_expected_fidelity'])\n",
+ "ax = df_filtered_and_sorted_expected_fidelity.plot(x=\"file_path\", y=\"expected_expected_fidelity_max_other\", label=\"Best\", rot=90, kind=plot_kind, color=\"green\", figsize=(30,10))\n",
+ "df_filtered_and_sorted_expected_fidelity.plot(x=\"file_path\", y=\"MQTPredictor_expected_fidelity_expected_fidelity\", label=\"MQT Predictor\", kind=plot_kind, rot=90, ax=ax, color=\"blue\")\n",
+ "df_filtered_and_sorted_expected_fidelity.plot(x=\"file_path\", y=\"expected_expected_fidelity_median\", kind=plot_kind, rot=90, ax=ax, color=\"orange\", label=\"Median\")\n",
+ "df_filtered_and_sorted_expected_fidelity.plot(x=\"file_path\", y=\"expected_expected_fidelity_min_other\", label=\"Worst\", rot=90, kind=plot_kind, ax=ax, color=\"red\")\n",
+ "\n",
+ "if plot_kind == \"line\":\n",
+ " plt.xticks(range(len(df_filtered_and_sorted_expected_fidelity.file_path)), df_filtered_and_sorted_expected_fidelity.file_path)\n",
+ "\n",
+ "plt.xticks(\n",
+ " list(range(0, len(df_filtered_and_sorted_expected_fidelity.file_path), 1)),\n",
+ " [ \"\" for i in range(0, len(df_filtered_and_sorted_expected_fidelity.file_path), 1)],\n",
+ " fontsize=16,\n",
+ ")\n",
+ "\n",
+ "plt.xlabel(\"Benchmarks\")\n",
+ "plt.ylabel(\"Expected expected_fidelity\")\n",
+ "\n",
+ "handles, labels = plt.gca().get_legend_handles_labels()\n",
+ "order = [1,0,2,3]\n",
+ "plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order]) \n",
+ "\n",
+ "plt.savefig(\"results/expected_fidelity_plot.pdf\", bbox_inches='tight')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2ded0ef5-8852-4375-9eec-4dfd6779d5bc",
+ "metadata": {},
+ "source": [
+ "## Top 3 expected_fidelity"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3a114c67-fd76-4c92-b320-fdb6e686914e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_filtered_and_sorted_expected_fidelity['Rank_MQT_expected_fidelity'] = df_filtered_and_sorted_expected_fidelity.apply(lambda row: sum(1 for col in df_filtered_and_sorted_expected_fidelity.columns if col.endswith('_expected_fidelity') and not \"MQT\" in col and row['MQTPredictor_expected_fidelity_expected_fidelity'] >=row[col]), axis=1)\n",
+ "df_filtered_and_sorted_expected_fidelity['Rank_MQT_expected_fidelity'] = len([col for col in df_filtered_and_sorted_expected_fidelity.columns if col.endswith('_expected_fidelity') and not \"MQT\" in col]) - df_filtered_and_sorted_expected_fidelity.Rank_MQT_expected_fidelity\n",
+ "plt.hist(df_filtered_and_sorted_expected_fidelity.Rank_MQT_expected_fidelity.values, bins=range(0,15,1), align=\"left\", weights=np.ones(len(df_filtered_and_sorted_expected_fidelity)) / len(df_filtered_and_sorted_expected_fidelity))\n",
+ "plt.xticks(range(0,14,1));"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2868e984-55f7-4aa5-a5e7-1c23e536d30e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "len(df_filtered_and_sorted_expected_fidelity[df_filtered_and_sorted_expected_fidelity['Rank_MQT_expected_fidelity']==0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0af212b6-a835-4594-b284-ae005e2f498b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "len(df_filtered_and_sorted_expected_fidelity)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "18a2c7fe-cf66-481b-8ca4-84811372d959",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "count_top3 = 0\n",
+ "for index, row in df_filtered_and_sorted_expected_fidelity.iterrows():\n",
+ " if row['Rank_MQT_expected_fidelity'] in [0, 1, 2]:\n",
+ " count_top3 += 1\n",
+ " \n",
+ "print(\"Percentage of Top-3:\", count_top3/len(df_filtered_and_sorted_expected_fidelity))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8afb822e-f064-4678-9d2e-df6a8b505f92",
+ "metadata": {},
+ "source": [
+ "# Critical Depth"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "11c66682-f7b6-4b6b-9ec8-35ea240d32c5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "predictor = ml.Predictor()\n",
+ "training_data = predictor.get_prepared_training_data(figure_of_merit=\"critical_depth\", save_non_zero_indices=True)\n",
+ "indices_test = training_data.indices_test\n",
+ "names_list = training_data.names_list\n",
+ "\n",
+ "test_benchmarks_critical_depth = [names_list[index_test] for index_test in indices_test]\n",
+ "df_filtered_critical_depth = df[df[\"file_path\"].isin(test_benchmarks_critical_depth)]\n",
+ "df_filtered_critical_depth = df_filtered_critical_depth[df_filtered_critical_depth[\"MQTPredictor_critical_depth_critical_depth\"]>=0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64a5c6ea-e092-4225-b356-9e1c9b8e40b8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"Crit. Dep. Improvement: \", df_filtered_critical_depth.loc[df['MQTPredictor_critical_depth_critical_depth'] != np.inf, 'MQTPredictor_critical_depth_critical_depth'].max())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f86663c8-ba0c-4e5f-a810-c6cab4744487",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "len(df_filtered_critical_depth)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1566bcf3-397e-44ec-88e6-beb159e1b4b2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plot_kind = \"line\"\n",
+ "df_filtered_and_sorted_critical_depth = df_filtered_critical_depth.sort_values(by=['MQTPredictor_critical_depth_critical_depth'])\n",
+ "ax = df_filtered_and_sorted_critical_depth.plot(x=\"file_path\", y=\"critical_depth_max_other\", label=\"Best\", rot=90, kind=plot_kind, color=\"green\", figsize=(30,10))\n",
+ "df_filtered_and_sorted_critical_depth.plot(x=\"file_path\", y=\"MQTPredictor_critical_depth_critical_depth\", kind=plot_kind, rot=90, ax=ax, color=\"blue\", label=\"MQT Predictor\")\n",
+ "df_filtered_and_sorted_critical_depth.plot(x=\"file_path\", y=\"critical_depth_median\", kind=plot_kind, rot=90, ax=ax, color=\"orange\", label=\"Median\")\n",
+ "df_filtered_and_sorted_critical_depth.plot(x=\"file_path\", y=\"critical_depth_min_other\", label=\"Worst\", rot=90, kind=plot_kind, ax=ax, color=\"red\")\n",
+ "\n",
+ "\n",
+ "if plot_kind == \"line\":\n",
+ " plt.xticks(range(len(df_filtered_and_sorted_critical_depth.file_path)), df_filtered_and_sorted_critical_depth.file_path);\n",
+ "\n",
+ "\n",
+ "plt.xticks(\n",
+ " list(range(0, len(df_filtered_and_sorted_critical_depth.file_path), 1)),\n",
+ " [\"\" for i in range(0, len(df_filtered_and_sorted_critical_depth), 1)],\n",
+ " fontsize=18,\n",
+ ")\n",
+ "\n",
+ "plt.xlabel(\"Benchmarks\")\n",
+ "plt.ylabel(\"Critical Depth\")\n",
+ "\n",
+ "handles, labels = plt.gca().get_legend_handles_labels()\n",
+ "order = [1,0,2,3]\n",
+ "plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order]) \n",
+ "\n",
+ "plt.savefig(\"results/critical_depth_plot.pdf\", bbox_inches='tight')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9941a75a-abb0-45eb-b850-8c948b411f37",
+ "metadata": {},
+ "source": [
+ "## Top 3 Critical Depth"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "71d74e70-8ec1-4ea6-a770-4726a6a04d6e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_filtered_and_sorted_critical_depth['Rank_MQT_critical_depth'] = df_filtered_and_sorted_critical_depth.apply(lambda row: sum(1 for col in df_filtered_and_sorted_critical_depth.columns if col.endswith('_critical_depth') and not \"MQT\" in col and row['MQTPredictor_critical_depth_critical_depth'] >= row[col]), axis=1)\n",
+ "df_filtered_and_sorted_critical_depth['Rank_MQT_critical_depth'] = len([col for col in df_filtered_and_sorted_critical_depth.columns if col.endswith('_critical_depth') and not \"MQT\" in col]) - df_filtered_and_sorted_critical_depth.Rank_MQT_critical_depth\n",
+ "plt.hist(df_filtered_and_sorted_critical_depth.Rank_MQT_critical_depth.values, bins=range(0,15,1), align=\"left\", weights=np.ones(len(df_filtered_and_sorted_critical_depth)) / len(df_filtered_and_sorted_critical_depth))\n",
+ "plt.xticks(range(0,14,1));"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6baa9861-4e45-4b1d-86bb-22cfd6b28761",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "len(df_filtered_and_sorted_critical_depth[df_filtered_and_sorted_critical_depth['Rank_MQT_critical_depth']==0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "48f6464b-823a-4169-b5ab-03c11f7966cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "len(df_filtered_and_sorted_critical_depth)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8370efe4-7039-4563-8f3a-62e3f6eda620",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "count_top3 = 0\n",
+ "for index, row in df_filtered_and_sorted_critical_depth.iterrows():\n",
+ " if row['Rank_MQT_critical_depth'] in [0, 1, 2]:\n",
+ " count_top3 += 1\n",
+ "\n",
+ "# Print the total count\n",
+ "print(\"Percentage of Top-3:\", count_top3/len(df_filtered_and_sorted_critical_depth))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4c6ae124-6ca5-4d7d-87e5-eb277b813b68",
+ "metadata": {},
+ "source": [
+ "# GHZ Evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91e19ce9-82c3-40fe-83ab-5e5310596f72",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(ml.helper.get_path_results(ghz_results=True), sep=',')\n",
+ "df = df[df.num_qubits<32]\n",
+ "plt.rcParams[\"font.size\"] = 50"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d357af6f-5afc-4d12-ba5e-2a7f0a1f7610",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df['expected_fidelity_median'] = df.apply(calculate_median, column_name=\"_expected_fidelity\",axis=1)\n",
+ "df['critical_depth_median'] = df.apply(calculate_median, column_name=\"_critical_depth\",axis=1)\n",
+ "df['expected_fidelity_min_other'] = df.apply(lambda row: row[[col for col in row.index if col.endswith('_expected_fidelity') and 'MQT' not in col]].replace(-1, np.nan).min(skipna=True), axis=1)\n",
+ "df['expected_fidelity_max_other'] = df.apply(lambda row: row[[col for col in row.index if col.endswith('_expected_fidelity') and 'MQT' not in col]].max(), axis=1)\n",
+ "df['critical_depth_min_other'] = df.apply(lambda row: row[[col for col in row.index if col.endswith('_critical_depth') and 'MQT' not in col]].replace(-1, np.nan).min(skipna=True), axis=1)\n",
+ "df['critical_depth_max_other'] = df.apply(lambda row: row[[col for col in row.index if col.endswith('_critical_depth') and 'MQT' not in col]].max(), axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "75ee34f6-32cb-4176-b557-962f7605ef8d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plot_kind = \"line\"\n",
+ "df = df.sort_values(by=['num_qubits'])\n",
+ "ax = df.plot(x=\"file_path\", y=\"expected_fidelity_max_other\", label=\"Best\", rot=90, kind=plot_kind, color=\"green\", figsize=(20,10))\n",
+ "df.plot(x=\"file_path\", y=\"MQTPredictor_expected_fidelity_expected_fidelity\", label=\"MQT Predictor\", kind=plot_kind, rot=90, ax=ax, color=\"blue\")\n",
+ "df.plot(x=\"file_path\", y=\"expected_fidelity_median\", kind=plot_kind, rot=90, ax=ax, color=\"orange\", label=\"Median\")\n",
+ "df.plot(x=\"file_path\", y=\"expected_fidelity_min_other\", label=\"Worst\", rot=90, kind=plot_kind, ax=ax, color=\"red\")\n",
+ "\n",
+ "if plot_kind == \"line\":\n",
+ " plt.xticks(range(len(df.file_path)), df.file_path)\n",
+ "\n",
+ "plt.xticks(\n",
+ " list(range(0, len(df), 1)),\n",
+ " [df.iloc[i].num_qubits if i % 4 == 1 else \"\" for i in range(len(df))],\n",
+ ")\n",
+ "\n",
+ "plt.xlabel(\"Number of Qubits\")\n",
+ "plt.ylabel(\"Expected Fidelity\")\n",
+ "\n",
+ "handles, labels = plt.gca().get_legend_handles_labels()\n",
+ "order = [1,0,2,3]\n",
+ "plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order]) \n",
+ "\n",
+ "\n",
+ "plt.savefig(\"results/expected_fidelity_ghz_plot.pdf\", bbox_inches='tight')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a6f1454e-dedf-421d-a347-badb32ccbb5a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plot_kind = \"line\"\n",
+ "df = df.sort_values(by=['num_qubits'])\n",
+ "ax = df.plot(x=\"file_path\", y=\"critical_depth_max_other\", label=\"Best\", rot=90, kind=plot_kind, color=\"green\", figsize=(20,10))\n",
+ "df.plot(x=\"file_path\", y=\"MQTPredictor_critical_depth_critical_depth\", kind=plot_kind, rot=90, ax=ax, color=\"blue\", label=\"MQT Predictor\")\n",
+ "df.plot(x=\"file_path\", y=\"critical_depth_median\", kind=plot_kind, rot=90, ax=ax, color=\"orange\", label=\"Median\")\n",
+ "\n",
+ "if plot_kind == \"line\":\n",
+ " plt.xticks(range(len(df.file_path)), df.file_path);\n",
+ "\n",
+ "plt.xticks(\n",
+ " list(range(0, len(df), 1)),\n",
+ " [df.iloc[i].num_qubits if i % 4 == 1 else \"\" for i in range(len(df))],\n",
+ ")\n",
+ "\n",
+ "plt.xlabel(\"Number of Qubits\")\n",
+ "plt.ylabel(\"Critical Depth\")\n",
+ "\n",
+ "handles, labels = plt.gca().get_legend_handles_labels()\n",
+ "order = [1,0,2]\n",
+ "plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order]) \n",
+ "\n",
+ "plt.savefig(\"results/critical_depth_ghz_plot.pdf\", bbox_inches='tight')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/ml/results/RandomForestClassifier.pdf b/notebooks/evalutions/results/critical_depth_ghz_plot.pdf
similarity index 59%
rename from notebooks/ml/results/RandomForestClassifier.pdf
rename to notebooks/evalutions/results/critical_depth_ghz_plot.pdf
index 5e50af987..9700f2654 100644
Binary files a/notebooks/ml/results/RandomForestClassifier.pdf and b/notebooks/evalutions/results/critical_depth_ghz_plot.pdf differ
diff --git a/notebooks/evalutions/results/critical_depth_plot.pdf b/notebooks/evalutions/results/critical_depth_plot.pdf
new file mode 100644
index 000000000..d1b0703fb
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diff --git a/notebooks/ml/results/GradientBoostingClassifier.pdf b/notebooks/evalutions/results/expected_fidelity_ghz_plot.pdf
similarity index 59%
rename from notebooks/ml/results/GradientBoostingClassifier.pdf
rename to notebooks/evalutions/results/expected_fidelity_ghz_plot.pdf
index 4530288bd..1934bf1a3 100644
Binary files a/notebooks/ml/results/GradientBoostingClassifier.pdf and b/notebooks/evalutions/results/expected_fidelity_ghz_plot.pdf differ
diff --git a/notebooks/evalutions/results/expected_fidelity_plot.pdf b/notebooks/evalutions/results/expected_fidelity_plot.pdf
new file mode 100644
index 000000000..7b616cdfb
Binary files /dev/null and b/notebooks/evalutions/results/expected_fidelity_plot.pdf differ
diff --git a/notebooks/ml/evaluation.ipynb b/notebooks/evalutions/supervised_ml_models/evaluation.ipynb
similarity index 93%
rename from notebooks/ml/evaluation.ipynb
rename to notebooks/evalutions/supervised_ml_models/evaluation.ipynb
index 3b1ae1706..2e93edf02 100644
--- a/notebooks/ml/evaluation.ipynb
+++ b/notebooks/evalutions/supervised_ml_models/evaluation.ipynb
@@ -16,26 +16,31 @@
"outputs": [],
"source": [
"from __future__ import absolute_import\n",
- "from numpy import asarray, save\n",
- "from mqt.predictor import utils, ml\n",
- "\n",
+ "from mqt.predictor import ml\n",
+ "from sklearn.model_selection import GridSearchCV\n",
+ "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
+ "from sklearn import tree, svm\n",
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "from sklearn.naive_bayes import GaussianNB\n",
+ "from sklearn.neural_network import MLPClassifier\n",
+ "import matplotlib.pyplot as plt\n",
"import numpy as np\n",
- "np.random.seed(10)\n",
"\n",
- "from sklearn import preprocessing\n",
- "from sklearn.model_selection import train_test_split\n",
- "from sklearn.model_selection import GridSearchCV\n",
+ "np.random.seed(10)\n",
"\n",
"predictor = ml.Predictor()\n",
+ "figure_of_merit=\"expected_fidelity\"\n",
"\n",
- "(X_train,\n",
- "X_test,\n",
- "y_train,\n",
- "y_test,\n",
- "indices_train,\n",
- "indices_test,\n",
- "names_list,\n",
- "scores_list) = predictor.get_prepared_training_data(save_non_zero_indices=True)\n",
+ "training_data = predictor.get_prepared_training_data(figure_of_merit=figure_of_merit, save_non_zero_indices=True)\n",
+ "\n",
+ "X_train = training_data.X_train\n",
+ "X_test = training_data.X_test\n",
+ "y_train = training_data.y_train\n",
+ "y_test = training_data.y_test\n",
+ "indices_train = training_data.indices_train\n",
+ "indices_test= training_data.indices_test\n",
+ "names_list= training_data.names_list\n",
+ "scores_list= training_data.scores_list\n",
"\n",
"scores_filtered = [scores_list[i] for i in indices_test]\n",
"names_filtered = [names_list[i] for i in indices_test]\n",
@@ -54,6 +59,16 @@
"color2 = \"#440154\""
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2dddba2b-afda-4ae3-89d2-0c54b6056756",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_train"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -79,11 +94,7 @@
"metadata": {},
"outputs": [],
"source": [
- "from sklearn.ensemble import RandomForestClassifier\n",
- "\n",
- "\n",
"clf = RandomForestClassifier(random_state=0)\n",
- "\n",
"tree_param = [\n",
" {\n",
" \"n_estimators\": [100, 200, 500],\n",
@@ -93,10 +104,8 @@
" \"bootstrap\": [True, False],\n",
" },\n",
"]\n",
- "\n",
"clf = GridSearchCV(clf, tree_param, cv=5, n_jobs=8).fit(X_train, y_train)\n",
"\n",
- "\n",
"y_pred = np.array(list(clf.predict(X_test)))\n",
"res, rel_scores = predictor.calc_performance_measures(scores_filtered, y_pred, y_test)\n",
"predictor.plot_eval_histogram(res, filename=\"RandomForestClassifier\", color=color1)\n",
@@ -148,10 +157,7 @@
"metadata": {},
"outputs": [],
"source": [
- "import pandas as pd\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "path = ml.helper.get_path_trained_model() / \"non_zero_indices.npy\"\n",
+ "path = ml.helper.get_path_trained_model(figure_of_merit, return_non_zero_indices=True)\n",
"non_zero_indices = np.load(str(path), allow_pickle=True)\n",
" \n",
"openqasm_qc_list = ml.helper.get_openqasm_gates()\n",
@@ -224,8 +230,6 @@
"metadata": {},
"outputs": [],
"source": [
- "from sklearn.ensemble import GradientBoostingClassifier\n",
- "\n",
"clf = GradientBoostingClassifier()\n",
"\n",
"param_grid = {\n",
@@ -264,9 +268,6 @@
"metadata": {},
"outputs": [],
"source": [
- "from sklearn import tree\n",
- "from sklearn.model_selection import GridSearchCV\n",
- "\n",
"clf = tree.DecisionTreeClassifier(random_state=5)\n",
"\n",
"tree_param = [\n",
@@ -310,8 +311,6 @@
"metadata": {},
"outputs": [],
"source": [
- "from sklearn.neighbors import KNeighborsClassifier\n",
- "\n",
"clf = KNeighborsClassifier()\n",
"param_grid = dict(n_neighbors=range(1, 10, 1))\n",
"clf = GridSearchCV(clf, param_grid, cv=5, n_jobs=8).fit(X_train, y_train)\n",
@@ -345,8 +344,6 @@
"metadata": {},
"outputs": [],
"source": [
- "from sklearn.neural_network import MLPClassifier\n",
- "\n",
"clf = MLPClassifier(max_iter=1000)\n",
"\n",
"param_grid = {\n",
@@ -388,8 +385,6 @@
"metadata": {},
"outputs": [],
"source": [
- "from sklearn import svm\n",
- "\n",
"clf = svm.SVC()\n",
"param_grid = {\"C\": [0.1, 1, 10], \"gamma\": [1, 0.1, 0.01], \"kernel\": [\"rbf\", \"sigmoid\"]}\n",
"clf = GridSearchCV(clf, param_grid, cv=5, n_jobs=8).fit(X_train, y_train)\n",
@@ -423,8 +418,6 @@
"metadata": {},
"outputs": [],
"source": [
- "from sklearn.naive_bayes import GaussianNB\n",
- "\n",
"clf = GaussianNB()\n",
"param_grid = {\"var_smoothing\": np.logspace(0, -9, num=100)}\n",
"clf = GridSearchCV(clf, param_grid, cv=5, n_jobs=8).fit(X_train, y_train)\n",
@@ -460,7 +453,7 @@
"source": [
"print(performance)\n",
"\n",
- "filename = \"results/performances.csv\"\n",
+ "filename = \"results/performances_\" + figure_of_merit + \".csv\"\n",
"with open(filename, \"w\") as f:\n",
" f.write(\"Classifier, Accuracy, Top3, Worst Rank, Eval. Score Diff., Std\\n\")\n",
" for sublist in performance:\n",
diff --git a/notebooks/example.ipynb b/notebooks/example.ipynb
new file mode 100644
index 000000000..a050f3691
--- /dev/null
+++ b/notebooks/example.ipynb
@@ -0,0 +1,554 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "ea3d6be5-b9ee-4375-adee-8fa2cb690fca",
+ "metadata": {},
+ "source": [
+ "# MQT Predictor Usage"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "697e4c2b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from mqt.predictor import qcompile\n",
+ "from mqt.bench import get_benchmark"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "02841c4f-a865-452d-a5ca-085a64eccf00",
+ "metadata": {},
+ "source": [
+ "## Generated Uncompiled Quantum Circuit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "73c0a2f9-adbd-4f85-8843-b6ab2ef53da8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "qc = get_benchmark(\"ghz\", level=\"indep\", circuit_size=5)\n",
+ "qc.draw()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6ebf087c-b03f-4050-b452-ecf9d698f5e7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "qc_compiled, compilation_information, quantum_device = qcompile(qc)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dc5b2a05-c062-46d8-b75e-2bfde4795a19",
+ "metadata": {},
+ "source": [
+ "## Predicted Device"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b3f643db-f833-433c-87f8-65cdc5355304",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "quantum_device"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf93d18b-b61a-47ae-988c-590e25e2f203",
+ "metadata": {},
+ "source": [
+ "## Top 3 Bins"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e649aff4-7fb8-457f-9ec8-c1cd97bbc4ea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def bins_labels(bins, **kwargs):\n",
+ " bin_w = (max(bins) - min(bins)) / (len(bins) - 1)\n",
+ " plt.xticks(np.arange(min(bins)+bin_w/2, max(bins), bin_w), bins, **kwargs)\n",
+ " plt.xlim(bins[0], bins[-1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7efb6e96-22c3-4739-b480-75d7b9a42785",
+ "metadata": {},
+ "source": [
+ "## Calcualte Data Points"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "76d28b11-cf2b-4286-a09a-05098b0cfd84",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# def calculate_x_highest(row, column_name, index):\n",
+ "# columns = [col for col in row.index if col.endswith(column_name) and not \"MQT\" in col]\n",
+ "# values = sorted([value for col, value in row[columns].items() if value != -1], reverse=True)\n",
+ "# return values[index-1] if len(values) >= 3 else 0\n",
+ "\n",
+ "def calculate_median(row, column_name):\n",
+ " columns = [col for col in row.index if col.endswith(column_name) and not \"MQT\" in col]\n",
+ " values = [value for col, value in row[columns].items() if value != -1]\n",
+ " return np.median(values) if values else None\n",
+ "\n",
+ "\n",
+ "df['fidelity_median'] = df.apply(calculate_median, column_name=\"_fidelity\",axis=1)\n",
+ "df['critical_depth_median'] = df.apply(calculate_median, column_name=\"_critical_depth\",axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "00181293-891f-4d35-9d0b-197ca04c5cd8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df['fidelity_min_other'] = df.apply(lambda row: row[[col for col in row.index if col.endswith('_fidelity') and 'MQT' not in col]].replace(-1, np.nan).min(skipna=True), axis=1)\n",
+ "df['fidelity_max_other'] = df.apply(lambda row: row[[col for col in row.index if col.endswith('_fidelity') and 'MQT' not in col]].max(), axis=1)\n",
+ "df['critical_depth_min_other'] = df.apply(lambda row: row[[col for col in row.index if col.endswith('_critical_depth') and 'MQT' not in col]].replace(-1, np.nan).min(skipna=True), axis=1)\n",
+ "df['critical_depth_max_other'] = df.apply(lambda row: row[[col for col in row.index if col.endswith('_critical_depth') and 'MQT' not in col]].max(), axis=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "15623d11-8024-4d65-ac23-b468af76ab70",
+ "metadata": {},
+ "source": [
+ "## Normalize"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "86906f27-0c80-4f63-a8fe-c1310a38e106",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# divider_column_name = 'fidelity_max_other'\n",
+ "\n",
+ "# # Get the list of column names ending with \"_fidelity\"\n",
+ "# columns_to_divide = [col for col in df.columns if col.endswith(\"_fidelity\") or col in ['fidelity_min_other', 'fidelity_max_other', 'fidelity_median' ]]\n",
+ "\n",
+ "# # Iterate through each column and perform the division\n",
+ "# for col_name in columns_to_divide:\n",
+ "# df[col_name] = df[col_name].divide(df[divider_column_name])\n",
+ "\n",
+ "# divider_column_name = 'critical_depth_max_other'\n",
+ "\n",
+ "# # Get the list of column names ending with \"_fidelity\"\n",
+ "# columns_to_divide = [col for col in df.columns if col.endswith(\"_critical_depth\") or col in ['critical_depth_min_other', 'critical_depth_max_other', 'critical_depth_median' ]]\n",
+ "\n",
+ "# # Iterate through each column and perform the division\n",
+ "# for col_name in columns_to_divide:\n",
+ "# df[col_name] = df[col_name].divide(df[divider_column_name])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "87e9f7d1-f565-4d14-ad75-b0e8bb2820fe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"Fid Improvement: \", df_filtered_fidelity.loc[df['MQTPredictor_fidelity_fidelity'] != np.inf, 'MQTPredictor_fidelity_fidelity'].max())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1a83daed-b560-405f-867d-e2b568773b39",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "len(df_filtered_fidelity)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "03a3ad43-c57f-45fe-b0bc-e0476e504178",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_filtered_fidelity['MQTPredictor_fidelity_fidelity'].describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "74cea6af-5e3d-4350-9e81-004a96cf697e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plot_kind = \"line\"\n",
+ "df_filtered_and_sorted_fidelity = df_filtered_fidelity.sort_values(by=['MQTPredictor_fidelity_fidelity'])\n",
+ "ax = df_filtered_and_sorted_fidelity.plot(x=\"file_path\", y=\"fidelity_max_other\", label=\"Best\", rot=90, kind=plot_kind, color=\"green\", figsize=(30,10))\n",
+ "df_filtered_and_sorted_fidelity.plot(x=\"file_path\", y=\"MQTPredictor_fidelity_fidelity\", label=\"MQT Predictor\", kind=plot_kind, rot=90, ax=ax, color=\"blue\")\n",
+ "df_filtered_and_sorted_fidelity.plot(x=\"file_path\", y=\"fidelity_median\", kind=plot_kind, rot=90, ax=ax, color=\"orange\", label=\"Median\")\n",
+ "df_filtered_and_sorted_fidelity.plot(x=\"file_path\", y=\"fidelity_min_other\", label=\"Worst\", rot=90, kind=plot_kind, ax=ax, color=\"red\")\n",
+ "\n",
+ "if plot_kind == \"line\":\n",
+ " plt.xticks(range(len(df_filtered_and_sorted_fidelity.file_path)), df_filtered_and_sorted_fidelity.file_path)\n",
+ "\n",
+ "plt.xticks(\n",
+ " list(range(0, len(df_filtered_and_sorted_fidelity.file_path), 1)),\n",
+ " [ \"\" for i in range(0, len(df_filtered_and_sorted_fidelity.file_path), 1)],\n",
+ " fontsize=16,\n",
+ ")\n",
+ "#df_filtered_and_sorted_fidelity.iloc[i][\"num_qubits\"]\n",
+ "\n",
+ "\n",
+ "plt.xlabel(\"Benchmarks\")\n",
+ "plt.ylabel(\"Expected Fidelity\")\n",
+ "\n",
+ "handles, labels = plt.gca().get_legend_handles_labels()\n",
+ "order = [1,0,2,3]\n",
+ "plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order]) \n",
+ "\n",
+ "plt.savefig(\"results/fidelity_plot.pdf\", bbox_inches='tight')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2ded0ef5-8852-4375-9eec-4dfd6779d5bc",
+ "metadata": {},
+ "source": [
+ "## Top 3 Fidelity"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3a114c67-fd76-4c92-b320-fdb6e686914e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_filtered_and_sorted_fidelity['Rank_MQT_fidelity'] = df_filtered_and_sorted_fidelity.apply(lambda row: sum(1 for col in df_filtered_and_sorted_fidelity.columns if col.endswith('_fidelity') and not \"MQT\" in col and row['MQTPredictor_fidelity_fidelity'] >=row[col]), axis=1)\n",
+ "df_filtered_and_sorted_fidelity['Rank_MQT_fidelity'] = len([col for col in df_filtered_and_sorted_fidelity.columns if col.endswith('_fidelity') and not \"MQT\" in col]) - df_filtered_and_sorted_fidelity.Rank_MQT_fidelity\n",
+ "plt.hist(df_filtered_and_sorted_fidelity.Rank_MQT_fidelity.values, bins=range(0,15,1), align=\"left\", weights=np.ones(len(df_filtered_and_sorted_fidelity)) / len(df_filtered_and_sorted_fidelity))\n",
+ "plt.xticks(range(0,14,1));"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2868e984-55f7-4aa5-a5e7-1c23e536d30e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "len(df_filtered_and_sorted_fidelity[df_filtered_and_sorted_fidelity['Rank_MQT_fidelity']==0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0af212b6-a835-4594-b284-ae005e2f498b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "len(df_filtered_and_sorted_fidelity)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "18a2c7fe-cf66-481b-8ca4-84811372d959",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "count_top3 = 0\n",
+ "for index, row in df_filtered_and_sorted_fidelity.iterrows():\n",
+ " if row['Rank_MQT_fidelity'] in [0, 1, 2]:\n",
+ " count_top3 += 1\n",
+ " \n",
+ "print(\"Percentage of Top-3:\", count_top3/len(df_filtered_and_sorted_fidelity))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64a5c6ea-e092-4225-b356-9e1c9b8e40b8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"Crit. Dep. Improvement: \", df_filtered_critical_depth.loc[df['MQTPredictor_critical_depth_critical_depth'] != np.inf, 'MQTPredictor_critical_depth_critical_depth'].max())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f86663c8-ba0c-4e5f-a810-c6cab4744487",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "len(df_filtered_critical_depth)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1566bcf3-397e-44ec-88e6-beb159e1b4b2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plot_kind = \"line\"\n",
+ "df_filtered_and_sorted_critical_depth = df_filtered_critical_depth.sort_values(by=['MQTPredictor_critical_depth_critical_depth'])\n",
+ "ax = df_filtered_and_sorted_critical_depth.plot(x=\"file_path\", y=\"critical_depth_max_other\", label=\"Best\", rot=90, kind=plot_kind, color=\"green\", figsize=(30,10))\n",
+ "df_filtered_and_sorted_critical_depth.plot(x=\"file_path\", y=\"MQTPredictor_critical_depth_critical_depth\", kind=plot_kind, rot=90, ax=ax, color=\"blue\", label=\"MQT Predictor\")\n",
+ "df_filtered_and_sorted_critical_depth.plot(x=\"file_path\", y=\"critical_depth_median\", kind=plot_kind, rot=90, ax=ax, color=\"orange\", label=\"Median\")\n",
+ "df_filtered_and_sorted_critical_depth.plot(x=\"file_path\", y=\"critical_depth_min_other\", label=\"Worst\", rot=90, kind=plot_kind, ax=ax, color=\"red\")\n",
+ "\n",
+ "\n",
+ "if plot_kind == \"line\":\n",
+ " plt.xticks(range(len(df_filtered_and_sorted_critical_depth.file_path)), df_filtered_and_sorted_critical_depth.file_path);\n",
+ "\n",
+ "\n",
+ "plt.xticks(\n",
+ " list(range(0, len(df_filtered_and_sorted_critical_depth.file_path), 1)),\n",
+ " [\"\" for i in range(0, len(df_filtered_and_sorted_critical_depth), 1)],\n",
+ " fontsize=18,\n",
+ ")\n",
+ "#df_filtered_and_sorted_critical_depth.iloc[i][\"num_qubits\"]\n",
+ "plt.xlabel(\"Benchmarks\")\n",
+ "plt.ylabel(\"Critical Depth\")\n",
+ "\n",
+ "handles, labels = plt.gca().get_legend_handles_labels()\n",
+ "order = [1,0,2,3]\n",
+ "plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order]) \n",
+ "\n",
+ "plt.savefig(\"results/critical_depth_plot.pdf\", bbox_inches='tight')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9941a75a-abb0-45eb-b850-8c948b411f37",
+ "metadata": {},
+ "source": [
+ "## Top 3 Critical Depth"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "71d74e70-8ec1-4ea6-a770-4726a6a04d6e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_filtered_and_sorted_critical_depth['Rank_MQT_critical_depth'] = df_filtered_and_sorted_critical_depth.apply(lambda row: sum(1 for col in df_filtered_and_sorted_critical_depth.columns if col.endswith('_critical_depth') and not \"MQT\" in col and row['MQTPredictor_critical_depth_critical_depth'] >= row[col]), axis=1)\n",
+ "df_filtered_and_sorted_critical_depth['Rank_MQT_critical_depth'] = len([col for col in df_filtered_and_sorted_critical_depth.columns if col.endswith('_critical_depth') and not \"MQT\" in col]) - df_filtered_and_sorted_critical_depth.Rank_MQT_critical_depth\n",
+ "plt.hist(df_filtered_and_sorted_critical_depth.Rank_MQT_critical_depth.values, bins=range(0,15,1), align=\"left\", weights=np.ones(len(df_filtered_and_sorted_critical_depth)) / len(df_filtered_and_sorted_critical_depth))\n",
+ "plt.xticks(range(0,14,1));"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6baa9861-4e45-4b1d-86bb-22cfd6b28761",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "len(df_filtered_and_sorted_critical_depth[df_filtered_and_sorted_critical_depth['Rank_MQT_critical_depth']==0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "48f6464b-823a-4169-b5ab-03c11f7966cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "len(df_filtered_and_sorted_critical_depth)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8370efe4-7039-4563-8f3a-62e3f6eda620",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "count_top3 = 0\n",
+ "for index, row in df_filtered_and_sorted_critical_depth.iterrows():\n",
+ " if row['Rank_MQT_critical_depth'] in [0, 1, 2]:\n",
+ " count_top3 += 1\n",
+ "\n",
+ "# Print the total count\n",
+ "print(\"Percentage of Top-3:\", count_top3/len(df_filtered_and_sorted_critical_depth))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91e19ce9-82c3-40fe-83ab-5e5310596f72",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(ml.helper.get_path_trained_model() / 'res_GHZ.csv', sep=',')\n",
+ "df = df[df.num_qubits<32]\n",
+ "plt.rcParams[\"font.size\"] = 50"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d357af6f-5afc-4d12-ba5e-2a7f0a1f7610",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# df['fidelity_second_highest'] = df.apply(calculate_x_highest, column_name=\"_fidelity\", index=2, axis=1)\n",
+ "# df['fidelity_third_highest'] = df.apply(calculate_x_highest, column_name=\"_fidelity\", index=3, axis=1)\n",
+ "df['fidelity_median'] = df.apply(calculate_median, column_name=\"_fidelity\",axis=1)\n",
+ "\n",
+ "\n",
+ "# df['critical_depth_third_highest'] = df.apply(calculate_x_highest, column_name=\"_critical_depth\", index=3,axis=1)\n",
+ "df['critical_depth_median'] = df.apply(calculate_median, column_name=\"_critical_depth\",axis=1)\n",
+ "\n",
+ "df['fidelity_min_other'] = df.apply(lambda row: row[[col for col in row.index if col.endswith('_fidelity') and 'MQT' not in col]].replace(-1, np.nan).min(skipna=True), axis=1)\n",
+ "df['fidelity_max_other'] = df.apply(lambda row: row[[col for col in row.index if col.endswith('_fidelity') and 'MQT' not in col]].max(), axis=1)\n",
+ "df['critical_depth_min_other'] = df.apply(lambda row: row[[col for col in row.index if col.endswith('_critical_depth') and 'MQT' not in col]].replace(-1, np.nan).min(skipna=True), axis=1)\n",
+ "df['critical_depth_max_other'] = df.apply(lambda row: row[[col for col in row.index if col.endswith('_critical_depth') and 'MQT' not in col]].max(), axis=1)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "75ee34f6-32cb-4176-b557-962f7605ef8d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plot_kind = \"line\"\n",
+ "df = df.sort_values(by=['num_qubits'])\n",
+ "ax = df.plot(x=\"file_path\", y=\"fidelity_max_other\", label=\"Best\", rot=90, kind=plot_kind, color=\"green\", figsize=(20,10))\n",
+ "df.plot(x=\"file_path\", y=\"MQTPredictor_fidelity_fidelity\", label=\"MQT Predictor\", kind=plot_kind, rot=90, ax=ax, color=\"blue\")\n",
+ "df.plot(x=\"file_path\", y=\"fidelity_median\", kind=plot_kind, rot=90, ax=ax, color=\"orange\", label=\"Median\")\n",
+ "df.plot(x=\"file_path\", y=\"fidelity_min_other\", label=\"Worst\", rot=90, kind=plot_kind, ax=ax, color=\"red\")\n",
+ "\n",
+ "if plot_kind == \"line\":\n",
+ " plt.xticks(range(len(df.file_path)), df.file_path)\n",
+ "\n",
+ "plt.xticks(\n",
+ " list(range(0, len(df), 1)),\n",
+ " [df.iloc[i].num_qubits if i % 4 == 1 else \"\" for i in range(len(df))],\n",
+ ")\n",
+ "\n",
+ "plt.xlabel(\"Number of Qubits\")\n",
+ "plt.ylabel(\"Expected Fidelity\")\n",
+ "\n",
+ "handles, labels = plt.gca().get_legend_handles_labels()\n",
+ "order = [1,0,2,3]\n",
+ "plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order]) \n",
+ "\n",
+ "\n",
+ "plt.savefig(\"results/fidelity_ghz_plot.pdf\", bbox_inches='tight')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a6f1454e-dedf-421d-a347-badb32ccbb5a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plot_kind = \"line\"\n",
+ "df = df.sort_values(by=['num_qubits'])\n",
+ "ax = df.plot(x=\"file_path\", y=\"critical_depth_max_other\", label=\"Best\", rot=90, kind=plot_kind, color=\"green\", figsize=(20,10))\n",
+ "df.plot(x=\"file_path\", y=\"MQTPredictor_critical_depth_critical_depth\", kind=plot_kind, rot=90, ax=ax, color=\"blue\", label=\"MQT Predictor\")\n",
+ "df.plot(x=\"file_path\", y=\"critical_depth_median\", kind=plot_kind, rot=90, ax=ax, color=\"orange\", label=\"Median\")\n",
+ "\n",
+ "if plot_kind == \"line\":\n",
+ " plt.xticks(range(len(df.file_path)), df.file_path);\n",
+ "\n",
+ "plt.xticks(\n",
+ " list(range(0, len(df), 1)),\n",
+ " [df.iloc[i].num_qubits if i % 4 == 1 else \"\" for i in range(len(df))],\n",
+ ")\n",
+ "\n",
+ "plt.xlabel(\"Number of Qubits\")\n",
+ "plt.ylabel(\"Critical Depth\")\n",
+ "\n",
+ "handles, labels = plt.gca().get_legend_handles_labels()\n",
+ "order = [1,0,2]\n",
+ "plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order]) \n",
+ "\n",
+ "plt.savefig(\"results/critical_depth_ghz_plot.pdf\", bbox_inches='tight')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7b823498-55e1-47df-9b7b-f5f462f38c50",
+ "metadata": {},
+ "source": [
+ "## Predicted and Conducted Compilation Passes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c327466-7905-45b9-980a-f96ca8cffadb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "compilation_information"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "53953b24-353e-465e-b4f4-d7c0f2f0f0bf",
+ "metadata": {},
+ "source": [
+ "## Compiled Circuit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fe643f31-9d3e-4b7f-875a-af27e3dab5ed",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "qc_compiled.draw()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/ml/results/DecisionTreeClassifier.pdf b/notebooks/ml/results/DecisionTreeClassifier.pdf
deleted file mode 100644
index 72b03ff52..000000000
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diff --git a/notebooks/ml/results/GaussianNB.pdf b/notebooks/ml/results/GaussianNB.pdf
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index 90f3d9b74..000000000
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diff --git a/notebooks/ml/results/KNeighborsClassifier.pdf b/notebooks/ml/results/KNeighborsClassifier.pdf
deleted file mode 100644
index f4267dd32..000000000
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diff --git a/notebooks/ml/results/MLPClassifier.pdf b/notebooks/ml/results/MLPClassifier.pdf
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index 3da0b7da4..000000000
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diff --git a/notebooks/ml/results/SVM.pdf b/notebooks/ml/results/SVM.pdf
deleted file mode 100644
index a994be6ba..000000000
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diff --git a/notebooks/ml/results/feature_importances.pdf b/notebooks/ml/results/feature_importances.pdf
deleted file mode 100644
index cd2edd922..000000000
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diff --git a/notebooks/ml/results/performances.csv b/notebooks/ml/results/performances.csv
deleted file mode 100644
index 929051c5f..000000000
--- a/notebooks/ml/results/performances.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-Classifier, Accuracy, Top3, Worst Rank, Eval. Score Diff., Std
-Random Forest, 0.7692715756136183, 0.9556048834628191, 23, -0.0021, 0.0138
-Gradient Boosting, 0.7635538966180296, 0.9456159822419534, 23, -0.0022, 0.0138
-Decision Tree, 0.7335889605248276, 0.9245283018867925, 12, -0.0038, 0.0202
-Nearest Neighbor, 0.7050605135165705, 0.9400665926748057, 23, -0.0036, 0.0219
-Multilayer Perceptron, 0.6598314670286166, 0.8890122086570478, 23, -0.0064, 0.029
-Support Vector Machine, 0.620848320325755, 0.7569367369589345, 21, -0.0083, 0.0365
-Naive Bayes, 0.3320585906571655, 0.5482796892341842, 19, -0.0184, 0.0506
diff --git a/notebooks/ml/results/y_pred_eval_normed.pdf b/notebooks/ml/results/y_pred_eval_normed.pdf
deleted file mode 100644
index 79914e190..000000000
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diff --git a/notebooks/ml/runtime_comparison.ipynb b/notebooks/ml/runtime_comparison.ipynb
deleted file mode 100644
index 46d2ad861..000000000
--- a/notebooks/ml/runtime_comparison.ipynb
+++ /dev/null
@@ -1,254 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "d8f3bb66",
- "metadata": {},
- "source": [
- "# Compiling all options"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "56464b72",
- "metadata": {},
- "outputs": [],
- "source": [
- "from mqt.predictor import utils, ml\n",
- "from mqt.bench import get_benchmark\n",
- "from pathlib import Path\n",
- "\n",
- "import numpy as np\n",
- "import time\n",
- "\n",
- "pred = ml.Predictor()\n",
- "qc = get_benchmark(\"dj\", 1, 7)\n",
- "source_path = Path(\"runtime_comp\")\n",
- "target_path = Path(\"runtime_comp_compiled\")\n",
- "\n",
- "if not source_path.exists():\n",
- " source_path.mkdir()\n",
- "if not target_path.exists():\n",
- " target_path.mkdir()\n",
- " \n",
- " \n",
- "filename = \"dj_indep_qiskit_7.qasm\"\n",
- "qc.qasm(filename=str(source_path/filename))\n",
- "\n",
- "results = []\n",
- "for _ in range(5):\n",
- " start = time.time()\n",
- " pred.compile_all_circuits_for_qc(\n",
- " filename=filename, source_path=str(source_path), target_path=str(target_path), timeout=60\n",
- " )\n",
- " res = pred.generate_training_sample(file=filename.split(\".\")[0], path_uncompiled_circuit=str(source_path), path_compiled_circuits=str(target_path))\n",
- " end = time.time()\n",
- " diff_all_compile = end - start\n",
- " results.append(diff_all_compile)\n",
- "runtimes_all_comp = results"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7be7d782",
- "metadata": {},
- "source": [
- "# Using MQT Predictor"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b30845c2",
- "metadata": {},
- "outputs": [],
- "source": [
- "results = []\n",
- "for _ in range(5):\n",
- " start = time.time()\n",
- " prediction = pred.predict(str(source_path/filename))\n",
- " print(prediction)\n",
- " pred.compile_predicted_compilation_path(str(source_path/filename), prediction)\n",
- " end = time.time()\n",
- "\n",
- " diff_predictor = round((end - start), 3)\n",
- " results.append(diff_predictor)\n",
- "\n",
- "runtimes_predictor = results"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "866bf2c9",
- "metadata": {},
- "source": [
- "# Difference"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cf20c3ca",
- "metadata": {},
- "outputs": [],
- "source": [
- "runtimes_all_comp"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "88d04e0e",
- "metadata": {},
- "outputs": [],
- "source": [
- "runtimes_predictor"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "28ee9571",
- "metadata": {},
- "outputs": [],
- "source": [
- "diff = 1 - np.mean(runtimes_predictor) / np.mean(runtimes_all_comp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d679b6ff",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(\"Runtime all compilations: \", np.mean(runtimes_all_comp))\n",
- "print(\"Runtime Predictor: \", np.mean(runtimes_predictor))\n",
- "print(\"Percentual Difference: \", np.round(diff, 3))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b1c56dda",
- "metadata": {},
- "source": [
- "# Check many samples randomly"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "994910ce",
- "metadata": {},
- "outputs": [],
- "source": [
- "from mqt.predictor import utils, ml\n",
- "from mqt.bench import get_benchmark\n",
- "from pathlib import Path\n",
- "\n",
- "import numpy as np\n",
- "import time\n",
- "\n",
- "\n",
- "\n",
- "pred = ml.Predictor()\n",
- "\n",
- "source_path = ml.helper.get_path_training_circuits()\n",
- "target_path = Path(\"runtime_comp_compiled\")\n",
- "\n",
- "#if not source_path.exists():\n",
- "# source_path.mkdir()\n",
- "if not target_path.exists():\n",
- " target_path.mkdir()\n",
- " \n",
- " \n",
- "results = []\n",
- "\n",
- "file_list = list(ml.helper.get_path_training_circuits().glob(\"*.qasm\"))\n",
- "\n",
- "compile_time_all = []\n",
- "compile_time_pred = []\n",
- "for i in range(20):\n",
- " random_index = np.random.randint(len(file_list))\n",
- " filename = file_list[random_index]\n",
- " print(i, filename)\n",
- " \n",
- " start = time.time()\n",
- " pred.compile_all_circuits_for_qc(filename=str(filename.name), source_path=str(source_path), target_path=str(target_path), timeout=60000)\n",
- " res = pred.generate_training_sample(file=str(filename.stem), path_compiled_circuits=str(target_path))\n",
- " end = time.time()\n",
- " compile_time_all.append(end - start)\n",
- " \n",
- " \n",
- " start = time.time()\n",
- " prediction = pred.predict(str(source_path/filename))\n",
- " pred.compile_predicted_compilation_path(str(source_path/filename), prediction)\n",
- " end = time.time()\n",
- " compile_time_pred.append(end - start)\n",
- "runtimes_all_comp = results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1586b46b",
- "metadata": {},
- "outputs": [],
- "source": [
- "compile_time_all"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5b857329",
- "metadata": {},
- "outputs": [],
- "source": [
- "compile_time_pred"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "483becca",
- "metadata": {},
- "outputs": [],
- "source": [
- "rel = np.array(compile_time_all)/np.array(compile_time_pred)\n",
- "rel"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5461dd1d",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(np.median(rel), np.mean(rel))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/rl/evaluation.ipynb b/notebooks/rl/evaluation.ipynb
deleted file mode 100644
index 85517d58f..000000000
--- a/notebooks/rl/evaluation.ipynb
+++ /dev/null
@@ -1,428 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "697e4c2b",
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import numpy as np\n",
- "from mqt.predictor import rl\n",
- "df = pd.read_csv(rl.helper.get_path_trained_model() / 'res.csv', sep=',')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ab0daf50",
- "metadata": {},
- "outputs": [],
- "source": [
- "df[\"RL-Qiskit Fid\"]=df[\"RL_fidelity_fidelity\"]-df[\"qiskit_o3_fidelity\"]\n",
- "df[\"RL-TKET Fid\"]=df[\"RL_fidelity_fidelity\"]-df[\"tket_fidelity\"]\n",
- "df[\"RL-Qiskit Crit Depth\"]=df[\"RL_critical_depth_critical_depth\"]-df[\"qiskit_o3_critical_depth\"]\n",
- "df[\"RL-TKET Crit Depth\"]=df[\"RL_critical_depth_critical_depth\"]-df[\"tket_critical_depth\"]\n",
- "df[\"RL-Qiskit gate_ratio\"]=df[\"RL_gate_ratio_gate_ratio\"]-df[\"qiskit_o3_gate_ratio\"]\n",
- "df[\"RL-TKET gate_ratio\"]=df[\"RL_gate_ratio_gate_ratio\"]-df[\"tket_gate_ratio\"]\n",
- "df[\"RL-Qiskit mix\"]=df[\"RL_mix_mix\"]-df[\"qiskit_o3_mix\"]\n",
- "df[\"RL-TKET mix\"]=df[\"RL_mix_mix\"]-df[\"tket_mix\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c5cd9290",
- "metadata": {},
- "outputs": [],
- "source": [
- "num_bins = 60\n",
- "color_qiskit = \"#994F00\"\n",
- "color_TKET = \"#006CD1\"\n",
- "size = 14\n",
- "legendsize = 12\n",
- "max_val_bench = max([df.groupby([\"benchmark\"])[\"RL-Qiskit Fid\"].mean().max(), \n",
- " df.groupby([\"benchmark\"])[\"RL-TKET Fid\"].mean().max(), \n",
- " df.groupby([\"benchmark\"])[\"RL-Qiskit Crit Depth\"].mean().max(), \n",
- " df.groupby([\"benchmark\"])[\"RL-TKET Crit Depth\"].mean().max(),\n",
- " df.groupby([\"benchmark\"])[\"RL-Qiskit gate_ratio\"].mean().max(), \n",
- " df.groupby([\"benchmark\"])[\"RL-TKET gate_ratio\"].mean().max(),\n",
- " df.groupby([\"benchmark\"])[\"RL-Qiskit mix\"].mean().max(), \n",
- " df.groupby([\"benchmark\"])[\"RL-TKET mix\"].mean().max()])\n",
- "min_value_bench = min([df.groupby([\"benchmark\"])[\"RL-Qiskit Fid\"].mean().min(),\n",
- " df.groupby([\"benchmark\"])[\"RL-TKET Fid\"].mean().min(), \n",
- " df.groupby([\"benchmark\"])[\"RL-Qiskit Crit Depth\"].mean().min(), \n",
- " df.groupby([\"benchmark\"])[\"RL-TKET Crit Depth\"].mean().min(), \n",
- " df.groupby([\"benchmark\"])[\"RL-Qiskit gate_ratio\"].mean().min(), \n",
- " df.groupby([\"benchmark\"])[\"RL-TKET gate_ratio\"].mean().min(),\n",
- " df.groupby([\"benchmark\"])[\"RL-Qiskit mix\"].mean().min(), \n",
- " df.groupby([\"benchmark\"])[\"RL-TKET mix\"].mean().min()])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4c33ca40",
- "metadata": {},
- "outputs": [],
- "source": [
- "df.columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7f0d828d",
- "metadata": {},
- "outputs": [],
- "source": [
- "df[\"RL-Qiskit Fid\"][df[\"RL-Qiskit Fid\"] > 0.0 ].count()/len(df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "afe491b9",
- "metadata": {},
- "outputs": [],
- "source": [
- "df[\"RL-TKET Fid\"][df[\"RL-TKET Fid\"] > 0.0 ].count()/len(df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8e85c37c",
- "metadata": {},
- "outputs": [],
- "source": [
- "df[\"RL-Qiskit gate_ratio\"][df[\"RL-Qiskit gate_ratio\"] > 0.0 ].count()/len(df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1cf25e42",
- "metadata": {},
- "outputs": [],
- "source": [
- "df[\"RL-TKET gate_ratio\"][df[\"RL-TKET gate_ratio\"] > 0.0 ].count()/len(df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b49bf4e4",
- "metadata": {},
- "outputs": [],
- "source": [
- "df[\"RL-Qiskit Crit Depth\"][df[\"RL-Qiskit Crit Depth\"] > 0.0 ].count()/len(df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f9984f15",
- "metadata": {},
- "outputs": [],
- "source": [
- "df[\"RL-TKET Crit Depth\"][df[\"RL-TKET Crit Depth\"] > 0.0 ].count()/len(df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "070fdebf",
- "metadata": {},
- "outputs": [],
- "source": [
- "df[\"RL-Qiskit mix\"][df[\"RL-Qiskit mix\"] > 0.0 ].count()/len(df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "96c81558",
- "metadata": {},
- "outputs": [],
- "source": [
- "df[\"RL-TKET mix\"][df[\"RL-TKET mix\"] > 0.0 ].count()/len(df)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8f00cd6f",
- "metadata": {},
- "source": [
- "# Expected Fidelity"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c41eb1a7",
- "metadata": {},
- "outputs": [],
- "source": [
- "max_val = max([df[\"RL-Qiskit Fid\"].max(), df[\"RL-TKET Fid\"].max(), -df[\"RL-Qiskit Fid\"].min(), -df[\"RL-TKET Fid\"].min()])\n",
- "xrange = (-max_val-(max_val)/10, max_val+(max_val)/10)\n",
- "ax = df[\"RL-Qiskit Fid\"].hist(bins=num_bins, alpha=0.5, range=xrange, color=color_qiskit, weights=np.ones(len(df[\"RL-Qiskit Fid\"])) / len(df[\"RL-Qiskit Fid\"]), xlabelsize=size, ylabelsize=size)\n",
- "df[\"RL-TKET Fid\"].hist(bins=num_bins, ax=ax, alpha=0.5, range=xrange, color=color_TKET, grid=False, weights=np.ones(len(df[\"RL-TKET Fid\"])) / len(df[\"RL-TKET Fid\"]))\n",
- "ax.set_ylabel(\"Relative frequency\", size=size)\n",
- "ax.set_xlabel(\"Absolute reward difference\", size=size)\n",
- "ax.legend([\"compared to Qiskit\", \"compared to TKET\"], fontsize=legendsize, loc='upper right')\n",
- "ax.axvline(x=0, color= 'black', linewidth=1).get_figure().savefig('results/fidelity_hist.pdf', bbox_inches='tight')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "97a0c23a",
- "metadata": {},
- "outputs": [],
- "source": [
- "ax = df.groupby([\"benchmark\"])[\"RL-Qiskit Fid\"].mean().plot(kind=\"bar\", color=color_qiskit, alpha=0.5)\n",
- "df.groupby([\"benchmark\"])[\"RL-TKET Fid\"].mean().plot(kind=\"bar\", ax=ax, color=color_TKET, alpha=0.5)\n",
- "ax.set_ylabel(\"Average reward difference\", size=size)\n",
- "ax.set_xlabel(\"Benchmark\", size=size)\n",
- "ax.tick_params(axis='x', which='both', labelsize=size)\n",
- "ax.legend([\"compared to Qiskit\", \"compared to TKET\"], fontsize=legendsize, loc='upper right')\n",
- "ax.set_ylim(min_value_bench-0.01, max_val_bench+0.01)\n",
- "ax.tick_params(axis='both', labelsize=size)\n",
- "ax.axhline(y=0, color= 'black', linewidth=1).get_figure().savefig('results/fidelity_bench.pdf', bbox_inches='tight')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "14834d01",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(df[\"RL-Qiskit Fid\"].mean(), df[\"RL-TKET Fid\"].mean())"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "56ae0910",
- "metadata": {},
- "source": [
- "# Gate Ratio"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5c04800c",
- "metadata": {},
- "outputs": [],
- "source": [
- "max_val = max([df[\"RL-Qiskit gate_ratio\"].max(), df[\"RL-TKET gate_ratio\"].max(), -df[\"RL-Qiskit gate_ratio\"].min(), -df[\"RL-TKET gate_ratio\"].min()])\n",
- "xrange = (-max_val-(max_val)/10, max_val+(max_val)/10)\n",
- "ax = df[\"RL-Qiskit gate_ratio\"].hist(bins=num_bins, alpha=0.5, range=xrange, color=color_qiskit, weights=np.ones(len(df[\"RL-Qiskit gate_ratio\"])) / len(df[\"RL-Qiskit gate_ratio\"]), xlabelsize=size, ylabelsize=size)\n",
- "df[\"RL-TKET gate_ratio\"].hist(bins=num_bins, ax=ax,alpha=0.5, range=xrange, color=color_TKET, grid=False, weights=np.ones(len(df[\"RL-TKET gate_ratio\"])) / len(df[\"RL-TKET gate_ratio\"]))\n",
- "ax.set_ylabel(\"Relative frequency\", size=size)\n",
- "ax.set_xlabel(\"Absolute reward difference\", size=size)\n",
- "ax.legend([\"compared to Qiskit\", \"compared to TKET\"], fontsize=legendsize, loc='upper right')\n",
- "ax.axvline(x=0, color= 'black', linewidth=1).get_figure().savefig('results/gate_ratio_hist.pdf', bbox_inches='tight')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b9dbe323",
- "metadata": {},
- "outputs": [],
- "source": [
- "ax = df.groupby([\"benchmark\"])[\"RL-Qiskit gate_ratio\"].mean().plot(kind=\"bar\", color=color_qiskit, alpha=0.5)\n",
- "df.groupby([\"benchmark\"])[\"RL-TKET gate_ratio\"].mean().plot(kind=\"bar\", ax=ax, color=color_TKET, alpha=0.5)\n",
- "ax.set_ylabel(\"Average reward difference\", size=size)\n",
- "ax.set_xlabel(\"Benchmark\", size=size)\n",
- "ax.tick_params(axis='x', which='both', labelsize=size)\n",
- "ax.set_ylim(min_value_bench-0.01, max_val_bench+0.01)\n",
- "ax.tick_params(axis='both', labelsize=size)\n",
- "ax.legend([\"compared to Qiskit\", \"compared to TKET\"], fontsize=legendsize, loc='upper right')\n",
- "ax.axhline(y=0, color= 'black', linewidth=1).get_figure().savefig('results/gate_ratio_bench.pdf', bbox_inches='tight')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "43016d5f",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(df[\"RL-Qiskit gate_ratio\"].mean(), df[\"RL-TKET gate_ratio\"].mean())"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e5588478",
- "metadata": {},
- "source": [
- "# Critical Depth"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c592106d",
- "metadata": {},
- "outputs": [],
- "source": [
- "max_val = max([df[\"RL-Qiskit Crit Depth\"].max(), df[\"RL-TKET Crit Depth\"].max(), -df[\"RL-Qiskit Crit Depth\"].min(), -df[\"RL-TKET Crit Depth\"].min()])\n",
- "xrange = (-max_val-(max_val)/10, max_val+(max_val)/10)\n",
- "ax = df[\"RL-Qiskit Crit Depth\"].hist(bins=num_bins, alpha=0.5, range=xrange, color=color_qiskit, weights=np.ones(len(df[\"RL-Qiskit Crit Depth\"])) / len(df[\"RL-Qiskit Crit Depth\"]), xlabelsize=size, ylabelsize=size)\n",
- "df[\"RL-TKET Crit Depth\"].hist(bins=num_bins, ax=ax, alpha=0.5, range=xrange, grid=False, color=color_TKET, weights=np.ones(len(df[\"RL-TKET Crit Depth\"])) / len(df[\"RL-TKET Crit Depth\"]))\n",
- "ax.set_ylabel(\"Relative frequency\", size=size)\n",
- "ax.set_xlabel(\"Absolute reward difference\", size=size)\n",
- "ax.legend([\"compared to Qiskit\", \"compared to TKET\"], fontsize=legendsize, loc='upper right')\n",
- "ax.axvline(x=0, color= 'black', linewidth=1).get_figure().savefig('results/critical_depth_hist.pdf', bbox_inches='tight')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8db7d93e",
- "metadata": {},
- "outputs": [],
- "source": [
- "ax = df.groupby([\"benchmark\"])[\"RL-Qiskit Crit Depth\"].mean().plot(kind=\"bar\", color=color_qiskit, alpha=0.5)\n",
- "df.groupby([\"benchmark\"])[\"RL-TKET Crit Depth\"].mean().plot(kind=\"bar\", ax=ax, color=color_TKET, alpha=0.5)\n",
- "ax.set_ylabel(\"Average reward difference\", size=size)\n",
- "ax.set_xlabel(\"Benchmark\", size=size)\n",
- "ax.tick_params(axis='x', which='both', labelsize=size)\n",
- "ax.set_ylim(min_value_bench-0.01, max_val_bench+0.01)\n",
- "ax.tick_params(axis='both', labelsize=size)\n",
- "ax.legend([\"compared to Qiskit\", \"compared to TKET\"], fontsize=legendsize, loc='upper right')\n",
- "ax.axhline(y=0, color= 'black', linewidth=1).get_figure().savefig('results/critical_depth_bench.pdf', bbox_inches='tight')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e7b043b4",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(df[\"RL-Qiskit Crit Depth\"].mean(), df[\"RL-TKET Crit Depth\"].mean())"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6c869871",
- "metadata": {},
- "source": [
- "# Mix"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "09b90d94",
- "metadata": {},
- "outputs": [],
- "source": [
- "max_val = max([df[\"RL-Qiskit mix\"].max(), df[\"RL-TKET mix\"].max(), -df[\"RL-Qiskit mix\"].min(), -df[\"RL-TKET mix\"].min()])\n",
- "xrange = (-max_val-(max_val)/10, max_val+(max_val)/10)\n",
- "ax = df[\"RL-Qiskit mix\"].hist(bins=num_bins, alpha=0.5, range = xrange, color=color_qiskit, weights=np.ones(len(df[\"RL-Qiskit mix\"])) / len(df[\"RL-Qiskit mix\"]), xlabelsize=size, ylabelsize=size)\n",
- "df[\"RL-TKET mix\"].hist(bins=num_bins, ax=ax, alpha=0.5, range = xrange, grid=False, color=color_TKET, weights=np.ones(len(df[\"RL-TKET mix\"])) / len(df[\"RL-TKET mix\"]))\n",
- "ax.set_ylabel(\"Relative frequency\", size=size)\n",
- "ax.set_xlabel(\"Absolute reward difference\", size=size)\n",
- "ax.legend([\"compared to Qiskit\", \"compared to TKET\"], fontsize=legendsize, loc='upper right')\n",
- "ax.axvline(x=0, color= 'black', linewidth=1).get_figure().savefig('results/mix_hist.pdf', bbox_inches='tight')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7611c8a5",
- "metadata": {},
- "outputs": [],
- "source": [
- "ax = df.groupby([\"benchmark\"])[\"RL-Qiskit mix\"].mean().plot(kind=\"bar\", color=color_qiskit, alpha=0.5)\n",
- "df.groupby([\"benchmark\"])[\"RL-TKET mix\"].mean().plot(kind=\"bar\", ax=ax, color=color_TKET, alpha=0.5)\n",
- "ax.set_ylabel(\"Average reward difference\", size=size)\n",
- "ax.set_xlabel(\"Benchmark\", size=size)\n",
- "ax.tick_params(axis='x', which='both', labelsize=size)\n",
- "ax.set_ylim(min_value_bench-0.01, max_val_bench+0.01)\n",
- "ax.tick_params(axis='both', labelsize=size)\n",
- "ax.legend([\"compared to Qiskit\", \"compared to TKET\"], fontsize=legendsize, loc='upper right')\n",
- "ax.axhline(y=0, color= 'black', linewidth=1).get_figure().savefig('results/mix_bench.pdf', bbox_inches='tight')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7fe4a20d",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(df[\"RL-Qiskit mix\"].mean(), df[\"RL-TKET mix\"].mean())"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5cd42294",
- "metadata": {},
- "source": [
- "# 4x4 Matrix With Mean Results and RL Optimization Criterion Comparison"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6a887a2c",
- "metadata": {},
- "outputs": [],
- "source": [
- "RL_fidelity = [df[\"RL_fidelity_fidelity\"].mean(), df[\"RL_fidelity_gate_ratio\"].mean(), df[\"RL_fidelity_critical_depth\"].mean(), df[\"RL_fidelity_mix\"].mean()]\n",
- "RL_gate_ratio = [df[\"RL_gate_ratio_fidelity\"].mean(),df[\"RL_gate_ratio_gate_ratio\"].mean(),df[\"RL_gate_ratio_critical_depth\"].mean(), df[\"RL_gate_ratio_mix\"].mean()]\n",
- "RL_critical_depth = [ df[\"RL_critical_depth_fidelity\"].mean(),df[\"RL_critical_depth_gate_ratio\"].mean(), df[\"RL_critical_depth_critical_depth\"].mean(), df[\"RL_critical_depth_mix\"].mean()]\n",
- "RL_mix =[ df[\"RL_mix_fidelity\"].mean(),df[\"RL_mix_gate_ratio\"].mean(), df[\"RL_mix_critical_depth\"].mean(), df[\"RL_mix_mix\"].mean()]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c8d299e2",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(np.round(RL_fidelity,2))\n",
- "print(np.round(RL_gate_ratio,2))\n",
- "print(np.round(RL_critical_depth,2))\n",
- "print(np.round(RL_mix,2))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8f5f4fa2",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/rl/results/critical_depth_bench.pdf b/notebooks/rl/results/critical_depth_bench.pdf
deleted file mode 100644
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diff --git a/notebooks/rl/results/fidelity_bench.pdf b/notebooks/rl/results/fidelity_bench.pdf
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diff --git a/notebooks/rl/results/gate_ratio_hist.pdf b/notebooks/rl/results/gate_ratio_hist.pdf
deleted file mode 100644
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diff --git a/notebooks/rl/results/mix_bench.pdf b/notebooks/rl/results/mix_bench.pdf
deleted file mode 100644
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diff --git a/notebooks/rl/results/mix_hist.pdf b/notebooks/rl/results/mix_hist.pdf
deleted file mode 100644
index f641b2f1e..000000000
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diff --git a/pyproject.toml b/pyproject.toml
index 8bd20c936..e5046eb7d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,7 +19,7 @@ requires-python = ">=3.9"
dynamic = ["version"]
dependencies = [
- "mqt.bench>=0.4.4,<0.4.5",
+ "mqt.bench>=1.0.0,<1.1.0",
"sb3_contrib>=2.0.0",
"scikit-learn>=1.3.0, <1.3.1",
"importlib_metadata>=4.4; python_version < '3.10'",
diff --git a/src/mqt/predictor/Calibration.py b/src/mqt/predictor/Calibration.py
index 7dc595601..35b608f7d 100644
--- a/src/mqt/predictor/Calibration.py
+++ b/src/mqt/predictor/Calibration.py
@@ -14,55 +14,91 @@
class Calibration:
+ """The Calibration class is used to store calibration data for different devices."""
+
def __init__(self) -> None:
try:
- self.ibm_washington_cx_mean_error = get_mean_IBM_washington_cx_error()
self.ibm_montreal_calibration = FakeMontreal().properties()
self.ibm_washington_calibration = FakeWashington().properties()
self.oqc_lucy_calibration = parse_oqc_calibration_config()
self.rigetti_m2_calibration = parse_rigetti_calibration_config()
- self.ionq_calibration = parse_ionq_calibration_config()
+ self.ionq_harmony_calibration = parse_simple_calibration_config("ionq_harmony")
+ self.ionq_aria1_calibration = parse_simple_calibration_config("ionq_aria1")
+ self.quantinuum_h2_calibration = parse_simple_calibration_config("quantinuum_h2")
except Exception as e:
raise RuntimeError("Error in Calibration initialization: " + str(e)) from e
def get_mean_IBM_washington_cx_error() -> float:
+ """Returns the mean cx error for the IBM Washington device."""
cmap: list[list[int]] = FakeWashington().configuration().coupling_map
backend = FakeWashington().properties()
- somelist = [x for x in cmap if backend.gate_error("cx", x) < 1]
+ nonfaulty_connections = [x for x in cmap if backend.gate_error("cx", x) < 1]
+
+ res: list[float] = []
+ for elem in nonfaulty_connections:
+ res.append(backend.gate_error("cx", elem))
+
+ return cast(float, np.mean(res))
+
+
+def get_mean_IBM_montreal_cx_error() -> float:
+ """Returns the mean cx error for the IBM Washington device."""
+ cmap: list[list[int]] = FakeMontreal().configuration().coupling_map
+ backend = FakeMontreal().properties()
+ nonfaulty_connections = [x for x in cmap if backend.gate_error("cx", x) < 1]
res: list[float] = []
- for elem in somelist:
+ for elem in nonfaulty_connections:
res.append(backend.gate_error("cx", elem))
return cast(float, np.mean(res))
class DeviceCalibration(TypedDict):
+ """The DeviceCalibration class is used to store calibration data for different devices."""
+
backend: str
avg_1Q: float
avg_2Q: float
-def parse_ionq_calibration_config() -> DeviceCalibration:
- ref = resources.files("mqt.predictor") / "calibration_files" / "ionq_calibration.json"
+def parse_simple_calibration_config(device: str) -> DeviceCalibration:
+ """Parses the calibration data for the given device.
+
+ Args:
+ device (str): The name of the device.
+
+ Returns:
+ DeviceCalibration: The calibration data for the given device.
+ """
+
+ calibration_filename = device + "_calibration.json"
+ ref = resources.files("mqt.predictor") / "calibration_files" / calibration_filename
with ref.open() as f:
- ionq_calibration = json.load(f)
+ calibration = json.load(f)
return {
- "backend": "ionq",
- "avg_1Q": ionq_calibration["fidelity"]["1Q"].get("mean"),
- "avg_2Q": ionq_calibration["fidelity"]["2Q"].get("mean"),
+ "backend": device,
+ "avg_1Q": calibration["fidelity"]["1Q"].get("mean"),
+ "avg_2Q": calibration["fidelity"]["2Q"].get("mean"),
}
class OQCCalibration(DeviceCalibration):
+ """The OQCCalibration class is used to store calibration data for the OQC device."""
+
fid_1Q: dict[str, float]
fid_1Q_readout: dict[str, float]
fid_2Q: dict[str, float]
def parse_oqc_calibration_config() -> OQCCalibration:
+ """Parses the calibration data for the OQC device.
+
+ Returns:
+ OQCCalibration: The calibration data for the OQC device.
+ """
ref = resources.files("mqt.predictor") / "calibration_files" / "oqc_lucy_calibration.json"
with ref.open() as f:
oqc_lucy_calibration = json.load(f)
@@ -90,12 +126,19 @@ def parse_oqc_calibration_config() -> OQCCalibration:
class RigettiCalibration(DeviceCalibration):
+ """The RigettiCalibration class is used to store calibration data for the Rigetti Aspen M2 device."""
+
fid_1Q: dict[str, float]
fid_1Q_readout: dict[str, float]
fid_2Q_CZ: dict[str, float]
def parse_rigetti_calibration_config() -> RigettiCalibration:
+ """Parses the calibration data for the Rigetti Aspen M2 device.
+
+ Returns:
+ RigettiCalibration: The calibration data for the Rigetti Aspen M2 device.
+ """
ref = resources.files("mqt.predictor") / "calibration_files" / "rigetti_m2_calibration.json"
with ref.open() as f:
rigetti_m2_calibration = json.load(f)
diff --git a/src/mqt/predictor/Result.py b/src/mqt/predictor/Result.py
new file mode 100644
index 000000000..c0fd7186f
--- /dev/null
+++ b/src/mqt/predictor/Result.py
@@ -0,0 +1,49 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from mqt.predictor import reward
+
+if TYPE_CHECKING:
+ from qiskit import QuantumCircuit
+
+
+class Result:
+ """
+ The Result class is used to store the results of a compilation.
+
+ Attributes:
+ compilation_setup (str): The setup used for compilation. Either 'mqt-predictor_', 'qiskit' or 'tket'. For the two latter, also the device name is appended.
+ compilation_time (float): The time it took to compile the benchmark.
+ compiled_qc (QuantumCircuit | None): The compiled quantum circuit. If compilation failed, None is returned.
+ device (str): The device used for compilation.
+
+ """
+
+ def __init__(
+ self,
+ compilation_setup: str,
+ compilation_time: float,
+ compiled_qc: QuantumCircuit | None,
+ device: str,
+ ) -> None:
+ if compiled_qc is not None:
+ rew_fid = reward.expected_fidelity(compiled_qc, device)
+ rew_crit_depth = reward.crit_depth(compiled_qc)
+ else:
+ rew_fid = -1.0
+ rew_crit_depth = -1.0
+
+ self.compiler = compilation_setup
+ self.compilation_time = compilation_time
+ self.fidelity = rew_fid
+ self.critical_depth = rew_crit_depth
+
+ def get_dict(self) -> dict[str, float]:
+ """Returns the results as a dictionary."""
+
+ return {
+ self.compiler + "_" + "time": self.compilation_time,
+ self.compiler + "_" + "expected_fidelity": self.fidelity,
+ self.compiler + "_" + "critical_depth": self.critical_depth,
+ }
diff --git a/src/mqt/predictor/__init__.py b/src/mqt/predictor/__init__.py
index cf425b888..a22fbde8e 100644
--- a/src/mqt/predictor/__init__.py
+++ b/src/mqt/predictor/__init__.py
@@ -1,6 +1,13 @@
import logging
+from mqt.predictor.Result import Result
+from mqt.predictor.ml import qcompile
-logger = logging.getLogger("mqtpredictor")
+__all__ = [
+ "Result",
+ "qcompile",
+]
+
+logger = logging.getLogger("mqt-predictor")
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.DEBUG)
diff --git a/src/mqt/predictor/calibration_files/ionq_aria1_calibration.json b/src/mqt/predictor/calibration_files/ionq_aria1_calibration.json
new file mode 100644
index 000000000..c8e4fca99
--- /dev/null
+++ b/src/mqt/predictor/calibration_files/ionq_aria1_calibration.json
@@ -0,0 +1,30 @@
+{
+ "braketSchemaHeader": {
+ "name": "braket.device_schema.ionq.ionq_provider_properties",
+ "version": "1"
+ },
+ "fidelity": {
+ "1Q": {
+ "mean": 0.9996
+ },
+ "2Q": {
+ "mean": 0.9908
+ },
+ "spam": {
+ "mean": 0.9941
+ }
+ },
+ "timing": {
+ "T1": 100,
+ "T2": 1,
+ "1Q": 0.000135,
+ "2Q": 0.0006,
+ "readout": 0.0003,
+ "reset": 0.00002
+ },
+ "errorMitigation": {
+ "braket.device_schema.error_mitigation.debias.Debias": {
+ "minimumShots": 2500
+ }
+ }
+}
diff --git a/src/mqt/predictor/calibration_files/ionq_calibration.json b/src/mqt/predictor/calibration_files/ionq_harmony_calibration.json
similarity index 100%
rename from src/mqt/predictor/calibration_files/ionq_calibration.json
rename to src/mqt/predictor/calibration_files/ionq_harmony_calibration.json
diff --git a/src/mqt/predictor/calibration_files/quantinuum_h2_calibration.json b/src/mqt/predictor/calibration_files/quantinuum_h2_calibration.json
new file mode 100644
index 000000000..13e562857
--- /dev/null
+++ b/src/mqt/predictor/calibration_files/quantinuum_h2_calibration.json
@@ -0,0 +1,10 @@
+{
+ "fidelity": {
+ "1Q": {
+ "mean": 0.999
+ },
+ "2Q": {
+ "mean": 0.997
+ }
+ }
+}
diff --git a/src/mqt/predictor/evaluation.py b/src/mqt/predictor/evaluation.py
new file mode 100644
index 000000000..2e8dd0fb6
--- /dev/null
+++ b/src/mqt/predictor/evaluation.py
@@ -0,0 +1,197 @@
+from __future__ import annotations
+
+import logging
+import time
+from importlib import resources
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+from joblib import Parallel, delayed
+from mqt.bench.tket_helper import get_rebase
+from mqt.predictor import Result, ml, reward, rl
+from pytket import OpType
+from pytket.architecture import Architecture
+from pytket.extensions.qiskit import qiskit_to_tk, tk_to_qiskit
+from pytket.passes import (
+ FullPeepholeOptimise,
+ PlacementPass,
+ RoutingPass,
+)
+from pytket.placement import GraphPlacement
+from qiskit import QuantumCircuit, transpile
+
+logger = logging.getLogger("mqt-predictor")
+
+
+def create_qiskit_result(qc: QuantumCircuit, device: dict[str, Any] | None = None) -> Result:
+ """Creates a Result object for a given benchmark and device using qiskit for compilation.
+
+ Args:
+ benchmark (str): The path to the benchmark to be compiled.
+ device (dict[str, Any] | None, optional): The device to be used for compilation. Defaults to None.
+
+ Returns:
+ Result: Returns a Result object containing the compiled quantum circuit.
+ """
+ assert device is not None
+ if qc.num_qubits > device["max_qubits"]:
+ return Result("qiskit_", -1, None, device["name"])
+ start_time = time.time()
+ try:
+ transpiled_qc_qiskit = transpile(
+ qc,
+ basis_gates=device["native_gates"],
+ coupling_map=device["cmap"],
+ optimization_level=3,
+ seed_transpiler=1,
+ )
+ except Exception as e:
+ logger.warning("qiskit Transpile Error occurred for: " + device["name"] + " " + str(e))
+ return Result("qiskit_" + device["name"], -1, None, device["name"])
+ duration = time.time() - start_time
+ return Result("qiskit_" + device["name"], duration, transpiled_qc_qiskit, device["name"])
+
+
+def create_tket_result(
+ qc: QuantumCircuit,
+ device: dict[str, Any] | None = None,
+) -> Result:
+ """Creates a Result object for a given benchmark and device using tket for compilation.
+
+ Args:
+ benchmark (str): The path to the benchmark to be compiled.
+ device (dict[str, Any] | None, optional): The device to be used for compilation. Defaults to None.
+
+ Returns:
+ Result: Returns a Result object containing the compiled quantum circuit.
+ """
+ assert device is not None
+ if qc.num_qubits > device["max_qubits"]:
+ return Result("tket_" + device["name"], -1, None, device["name"])
+ tket_qc = qiskit_to_tk(qc)
+ arch = Architecture(device["cmap"])
+
+ native_rebase = get_rebase(device["name"].split("_")[0])
+ assert native_rebase is not None
+
+ start_time = time.time()
+ try:
+ native_rebase.apply(tket_qc)
+ FullPeepholeOptimise(target_2qb_gate=OpType.TK2).apply(tket_qc)
+ PlacementPass(GraphPlacement(arch)).apply(tket_qc)
+ RoutingPass(arch).apply(tket_qc)
+ native_rebase.apply(tket_qc)
+ duration = time.time() - start_time
+ transpiled_qc_tket = tk_to_qiskit(tket_qc)
+ except Exception as e:
+ logger.warning("tket Transpile Error occurred for: " + device["name"] + " " + str(e))
+ return Result("tket_" + device["name"], -1, None, device["name"])
+
+ return Result("tket_" + device["name"], duration, transpiled_qc_tket, device["name"])
+
+
+def create_mqtpredictor_result(qc: QuantumCircuit, figure_of_merit: reward.figure_of_merit, filename: str) -> Result:
+ dev_name = ml.helper.predict_device_for_figure_of_merit(qc, figure_of_merit)
+ """ Creates a Result object for a given benchmark and figure of merit using mqt-predictor for compilation.
+
+ Args:
+ benchmark (str): The path to the benchmark to be compiled.
+ figure_of_merit (reward.reward_functions): The figure of merit to be used for compilation.
+
+ Returns:
+ Result: Returns a Result object containing the compiled quantum circuit.
+ """
+ assert isinstance(dev_name, str)
+ dev_index = rl.helper.get_device_index_of_device(dev_name)
+ target_filename = filename.split("/")[-1].split(".qasm")[0] + "_" + figure_of_merit + "_" + str(dev_index)
+ combined_path_filename = ml.helper.get_path_training_circuits_compiled() / (target_filename + ".qasm")
+ if Path(combined_path_filename).exists():
+ qc = QuantumCircuit.from_qasm_file(combined_path_filename)
+ if qc:
+ return Result(
+ "mqt-predictor_" + figure_of_merit,
+ -1,
+ qc,
+ dev_name,
+ )
+ else:
+ try:
+ qc_compiled = ml.qcompile(qc, figure_of_merit=figure_of_merit)
+ if qc_compiled:
+ assert isinstance(qc_compiled, tuple)
+ return Result(
+ "mqt-predictor_" + figure_of_merit,
+ -1,
+ qc_compiled[0],
+ dev_name,
+ )
+
+ except Exception as e:
+ logger.warning("mqt-predictor Transpile Error occurred for: " + filename + " " + dev_name + " " + str(e))
+ return Result("mqt-predictor_" + figure_of_merit, -1, None, dev_name)
+
+
+def evaluate_all_sample_circuits() -> None:
+ """Evaluates all sample circuits and saves the results to a csv file."""
+ res_csv = []
+
+ results = Parallel(n_jobs=-1, verbose=3, backend="threading")(
+ delayed(evaluate_sample_circuit)(str(file))
+ for file in list(ml.helper.get_path_training_circuits().glob("*.qasm"))
+ )
+ res_csv.append(list(results[0].keys()))
+ for res in results:
+ res_csv.append(list(res.values()))
+ np.savetxt(
+ ml.helper.get_path_results(),
+ res_csv,
+ delimiter=",",
+ fmt="%s",
+ )
+
+
+def evaluate_GHZ_circuits() -> None:
+ """Evaluates all GHZ circuits and saves the results to a csv file."""
+ res_csv = []
+
+ path = Path(str(resources.files("mqt.predictor"))) / "ml" / "training_data" / "ghz"
+ results = Parallel(n_jobs=-1, verbose=3, backend="threading")(
+ delayed(evaluate_sample_circuit)(str(file)) for file in list(path.glob("*.qasm"))
+ )
+ res_csv.append(list(results[0].keys()))
+ for res in results:
+ res_csv.append(list(res.values()))
+ np.savetxt(
+ ml.helper.get_path_results(ghz_results=True),
+ res_csv,
+ delimiter=",",
+ fmt="%s",
+ )
+
+
+def evaluate_sample_circuit(filename: str) -> dict[str, Any]:
+ """Evaluates a given sample circuit and returns the results as a dictionary.
+
+ Args:
+ filename (str): The path to the sample circuit to be evaluated.
+
+ Returns:
+ dict[str, Any]: Returns a dictionary containing the results of the evaluation.
+ """
+ logger.info("Evaluate file: " + filename)
+
+ results: dict[str, Any] = {
+ "file_path": str(Path(filename).stem),
+ "benchmark_name": str(Path(filename).stem).replace("_", " ").split(" ")[0],
+ "num_qubits": str(Path(filename).stem).replace("_", " ").split(" ")[-1],
+ }
+ qc = QuantumCircuit.from_qasm_file(filename)
+ results.update(create_mqtpredictor_result(qc, "expected_fidelity", filename=filename).get_dict())
+ results.update(create_mqtpredictor_result(qc, "critical_depth", filename=filename).get_dict())
+
+ for _i, dev in enumerate(rl.helper.get_devices()):
+ results.update(create_qiskit_result(qc, dev).get_dict())
+ results.update(create_tket_result(qc, dev).get_dict())
+
+ return results
diff --git a/src/mqt/predictor/ml/Predictor.py b/src/mqt/predictor/ml/Predictor.py
index 0b13523e1..529284684 100644
--- a/src/mqt/predictor/ml/Predictor.py
+++ b/src/mqt/predictor/ml/Predictor.py
@@ -2,192 +2,218 @@
import logging
from pathlib import Path
-from typing import Any, cast
+from typing import TYPE_CHECKING, Any, cast
import matplotlib.pyplot as plt
import numpy as np
from joblib import Parallel, delayed, load
-from mqt.bench import qiskit_helper, tket_helper
-from mqt.predictor import ml, reward, utils
-from pytket.extensions.qiskit import tk_to_qiskit
+from mqt.predictor import ml, reward, rl, utils
from qiskit import QuantumCircuit
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV, train_test_split
+if TYPE_CHECKING:
+ from numpy._typing import NDArray
+
plt.rcParams["font.family"] = "Times New Roman"
-logger = logging.getLogger("mqtpredictor")
+logger = logging.getLogger("mqt-predictor")
class Predictor:
- def __init__(self, verbose: int = 0) -> None:
- if verbose == 1:
- lvl = logging.INFO
- elif verbose == 2:
- lvl = logging.DEBUG
- else:
- lvl = logging.WARNING
- logger.setLevel(lvl)
+ def __init__(self, logger_level: int = logging.INFO) -> None:
+ logger.setLevel(logger_level)
self.clf = None
def set_classifier(self, clf: RandomForestClassifier) -> None:
+ """Sets the classifier to the given classifier"""
self.clf = clf
- def compile_all_circuits_for_qc(
+ def compile_all_circuits_circuitwise(
self,
- filename: str,
- source_path: str = "",
- target_path: str = "",
- timeout: int = 10,
+ figure_of_merit: reward.figure_of_merit,
+ timeout: int,
+ source_path: Path | None = None,
+ target_path: Path | None = None,
logger_level: int = logging.INFO,
- ) -> bool:
- """Handles the creation of one training sample.
+ ) -> None:
+ """Compiles all circuits in the given directory with the given timeout and saves them in the given directory.
- Keyword arguments:
- filename -- qasm circuit sample filename
- source_path -- path to file
- target_path -- path to directory for compiled circuit
- timeout -- timeout in seconds
+ Args:
+ timeout (int): The timeout in seconds for the compilation of a single circuit
+ source_path (Path, optional): The path to the directory containing the circuits to be compiled. Defaults to None.
+ target_path (Path, optional): The path to the directory where the compiled circuits should be saved. Defaults to None.
+ logger_level (int, optional): The level of the logger. Defaults to logging.INFO.
- Return values:
- True -- at least one compilation option succeeded
- False -- if not
"""
-
logger.setLevel(logger_level)
- if not source_path:
- source_path = str(ml.helper.get_path_training_circuits())
- if not target_path:
- target_path = str(ml.helper.get_path_training_circuits_compiled())
+ if source_path is None:
+ source_path = ml.helper.get_path_training_circuits()
- logger.info("Processing: " + filename)
- qc = QuantumCircuit.from_qasm_file(Path(source_path) / filename)
+ if target_path is None:
+ target_path = ml.helper.get_path_training_circuits_compiled()
- if not qc:
- return False
+ Parallel(n_jobs=-1, verbose=100)(
+ delayed(self.generate_compiled_circuits_for_single_training_circuit)(
+ filename, timeout, source_path, target_path, figure_of_merit
+ )
+ for filename in source_path.iterdir()
+ )
- compilation_pipeline = ml.helper.get_compilation_pipeline()
+ def generate_compiled_circuits_for_single_training_circuit(
+ self,
+ filename: Path,
+ timeout: int,
+ source_path: Path,
+ target_path: Path,
+ figure_of_merit: reward.figure_of_merit,
+ ) -> None:
+ """Compiles a single circuit with the given timeout and saves it in the given directory.
- results = []
- comp_path_id = 0
+ Args:
+ filename (Path): The path to the circuit to be compiled
+ timeout (int): The timeout in seconds for the compilation of the circuit
+ source_path (Path): The path to the directory containing the circuit to be compiled
+ target_path (Path): The path to the directory where the compiled circuit should be saved
+ figure_of_merit (reward.figure_of_merit): The figure of merit to be used for compilation.
+
+ """
try:
- for gate_set_name, devices in compilation_pipeline["devices"].items():
- for device_name, max_qubits in devices:
- for compiler, settings in compilation_pipeline["compiler"].items():
- if "qiskit" in compiler:
- for opt_level in settings["optimization_level"]:
- target_filename = filename.split(".qasm")[0] + "_" + str(comp_path_id)
- comp_path_id += 1
- if max_qubits >= qc.num_qubits:
- tmp = utils.timeout_watcher(
- qiskit_helper.get_mapped_level,
- [
- qc,
- gate_set_name,
- qc.num_qubits,
- device_name,
- opt_level,
- False,
- False,
- target_path,
- target_filename,
- ],
- timeout,
- )
- results.append(tmp)
- if not tmp:
- continue
- elif "tket" in compiler:
- for lineplacement in settings["lineplacement"]:
- target_filename = filename.split(".qasm")[0] + "_" + str(comp_path_id)
- comp_path_id += 1
- if max_qubits >= qc.num_qubits:
- tmp = utils.timeout_watcher(
- tket_helper.get_mapped_level,
- [
- qc,
- gate_set_name,
- qc.num_qubits,
- device_name,
- lineplacement,
- False,
- False,
- target_path,
- target_filename,
- ],
- timeout,
- )
- results.append(tmp)
- if not tmp:
- continue
-
- if all(x is False for x in results):
- logger.debug("No compilation succeeded for this quantum circuit: " + filename)
- return False
- return True
+ qc = QuantumCircuit.from_qasm_file(Path(source_path) / filename)
+ if filename.suffix != ".qasm":
+ return
+
+ for i, dev in enumerate(rl.helper.get_devices()):
+ target_filename = str(filename).split("/")[-1].split(".qasm")[0] + "_" + figure_of_merit + "_" + str(i)
+ if (Path(target_path) / (target_filename + ".qasm")).exists() or qc.num_qubits > dev["max_qubits"]:
+ continue
+ try:
+ res = utils.timeout_watcher(rl.qcompile, [qc, figure_of_merit, dev["name"]], timeout)
+ if res:
+ compiled_qc = res[0]
+ compiled_qc.qasm(filename=Path(target_path) / (target_filename + ".qasm"))
+
+ except Exception as e:
+ print(e, filename, "inner")
except Exception as e:
- raise RuntimeError("Error during compilation: " + str(e)) from e
+ print(e, filename, "outer")
- def generate_compiled_circuits(
+ def compile_all_circuits_devicewise(
self,
- source_path: str = "",
- target_path: str = "",
- timeout: int = 10,
+ device_name: str,
+ timeout: int,
+ figure_of_merit: reward.figure_of_merit,
+ source_path: Path | None = None,
+ target_path: Path | None = None,
+ logger_level: int = logging.INFO,
) -> None:
- """Handles the creation of all training samples.
+ """Compiles all circuits in the given directory with the given timeout and saves them in the given directory.
+
+ Args:
+ device_name (str): The name of the device to be used for compilation
+ timeout (int): The timeout in seconds for the compilation of a single circuit
+ figure_of_merit (reward.reward_functions): The figure of merit to be used for compilation
+ source_path (Path, optional): The path to the directory containing the circuits to be compiled. Defaults to None.
+ target_path (Path, optional): The path to the directory where the compiled circuits should be saved. Defaults to None.
+ logger_level (int, optional): The level of the logger. Defaults to logging.INFO.
+ """
+ logger.setLevel(logger_level)
+
+ logger.info("Processing: " + device_name + " for " + figure_of_merit)
+ rl_pred = rl.Predictor(figure_of_merit=figure_of_merit, device_name=device_name)
+
+ dev_index = rl.helper.get_device_index_of_device(device_name)
+ dev_max_qubits = rl.helper.get_devices()[dev_index]["max_qubits"]
+
+ if source_path is None:
+ source_path = ml.helper.get_path_training_circuits()
- Keyword arguments:
- source_path -- path to file
- target_directory -- path to directory for compiled circuit
- timeout -- timeout in seconds
+ if target_path is None:
+ target_path = ml.helper.get_path_training_circuits_compiled()
+ for filename in source_path.iterdir():
+ if filename.suffix != ".qasm":
+ continue
+ qc = QuantumCircuit.from_qasm_file(Path(source_path) / filename)
+ if qc.num_qubits > dev_max_qubits:
+ continue
+
+ target_filename = (
+ str(filename).split("/")[-1].split(".qasm")[0] + "_" + figure_of_merit + "_" + str(dev_index)
+ )
+ if (Path(target_path) / (target_filename + ".qasm")).exists():
+ continue
+ try:
+ res = utils.timeout_watcher(rl.qcompile, [qc, figure_of_merit, device_name, rl_pred], timeout)
+ if res:
+ compiled_qc = res[0]
+ compiled_qc.qasm(filename=Path(target_path) / (target_filename + ".qasm"))
+
+ except Exception as e:
+ print(e, filename, device_name)
+ raise RuntimeError("Error during compilation: " + str(e)) from e
+
+ def generate_compiled_circuits(
+ self,
+ source_path: Path | None = None,
+ target_path: Path | None = None,
+ timeout: int = 600,
+ ) -> None:
+ """Compiles all circuits in the given directory with the given timeout and saves them in the given directory.
+
+ Args:
+ source_path (Path, optional): The path to the directory containing the circuits to be compiled. Defaults to None.
+ target_path (Path, optional): The path to the directory where the compiled circuits should be saved. Defaults to None.
+ timeout (int, optional): The timeout in seconds for the compilation of a single circuit. Defaults to 600.
"""
- if not source_path:
- source_path = str(ml.helper.get_path_training_circuits())
+ if source_path is None:
+ source_path = ml.helper.get_path_training_circuits()
- if not target_path:
- target_path = str(ml.helper.get_path_training_circuits_compiled())
+ if target_path is None:
+ target_path = ml.helper.get_path_training_circuits_compiled()
- path_zip = Path(source_path) / "mqtbench_training_samples.zip"
- if not any(file.suffix == ".qasm" for file in Path(source_path).iterdir()) and path_zip.exists():
+ path_zip = source_path / "training_data_device_selection.zip"
+ if not any(file.suffix == ".qasm" for file in source_path.iterdir()) and path_zip.exists():
import zipfile
with zipfile.ZipFile(str(path_zip), "r") as zip_ref:
zip_ref.extractall(source_path)
- Path(target_path).mkdir(exist_ok=True)
-
- source_circuits_list = [file.name for file in Path(source_path).iterdir() if file.suffix == ".qasm"]
+ target_path.mkdir(exist_ok=True)
- Parallel(n_jobs=-1, verbose=100)(
- delayed(self.compile_all_circuits_for_qc)(filename, source_path, target_path, timeout, logger.level)
- for filename in source_circuits_list
+ Parallel(n_jobs=1, verbose=100)(
+ delayed(self.compile_all_circuits_devicewise)(
+ device_name, timeout, figure_of_merit, source_path, target_path, logger.level
+ )
+ for figure_of_merit in ["expected_fidelity", "critical_depth"]
+ for device_name in [dev["name"] for dev in rl.helper.get_devices()]
)
def generate_trainingdata_from_qasm_files(
self,
- path_uncompiled_circuits: str = "",
- path_compiled_circuits: str = "",
- ) -> tuple[list[Any], list[Any], list[Any]]:
+ figure_of_merit: reward.figure_of_merit,
+ path_uncompiled_circuits: Path | None = None,
+ path_compiled_circuits: Path | None = None,
+ ) -> tuple[list[NDArray[np.float_]], list[str], list[NDArray[np.float_]]]:
"""Handles to create training data from all generated training samples
- Keyword arguments:
- path_uncompiled_circuits -- path to file
- path_compiled_circuits -- path to directory for compiled circuit
+ Args:
+ figure_of_merit (reward.reward_functions): The figure of merit to be used for training
+ path_uncompiled_circuits (Path, optional): The path to the directory containing the uncompiled circuits. Defaults to None.
+ path_compiled_circuits (Path, optional): The path to the directory containing the compiled circuits. Defaults to None.
+
+ Returns:
+ tuple[list[Any], list[Any], list[Any]]: The training data, consisting of training_data, name_list, scores_list
- Return values:
- training_data_ML_aggregated -- training data
- name_list -- names of all training samples
- scores -- evaluation scores for all compilation options
"""
if not path_uncompiled_circuits:
- path_uncompiled_circuits = str(ml.helper.get_path_training_circuits())
+ path_uncompiled_circuits = ml.helper.get_path_training_circuits()
if not path_compiled_circuits:
- path_compiled_circuits = str(ml.helper.get_path_training_circuits_compiled())
+ path_compiled_circuits = ml.helper.get_path_training_circuits_compiled()
# init resulting list (feature vector, name, scores)
training_data = []
@@ -196,18 +222,18 @@ def generate_trainingdata_from_qasm_files(
results = Parallel(n_jobs=-1, verbose=100)(
delayed(self.generate_training_sample)(
- str(filename.name),
+ filename.name,
path_uncompiled_circuits,
path_compiled_circuits,
+ figure_of_merit,
logger.level,
)
- for filename in Path(path_uncompiled_circuits).iterdir()
+ for filename in path_uncompiled_circuits.glob("*.qasm")
)
for sample in results:
- if not sample:
- continue
-
training_sample, circuit_name, scores = sample
+ if all(score == -1 for score in scores):
+ continue
training_data.append(training_sample)
name_list.append(circuit_name)
scores_list.append(scores)
@@ -216,77 +242,81 @@ def generate_trainingdata_from_qasm_files(
def generate_training_sample(
self,
- file: str,
- path_uncompiled_circuit: str = "",
- path_compiled_circuits: str = "",
- logger_level: int = logging.WARNING,
- ) -> tuple[tuple[list[Any], Any], str, list[float]] | bool:
- """Handles to create training data from a single generated training sample
-
- Keyword arguments:
- file -- filename for the training sample
- path_uncompiled_circuit -- path to file
- path_compiled_circuits -- path to directory for compiled circuit
-
- Return values:
- training_sample -- training data sample
- circuit_name -- names of the training sample circuit
- scores -- evaluation scores for all compilation options
+ file: Path,
+ path_uncompiled_circuit: Path,
+ path_compiled_circuits: Path,
+ figure_of_merit: reward.figure_of_merit = "expected_fidelity",
+ logger_level: int = logging.INFO,
+ ) -> tuple[tuple[list[Any], Any], str, list[float]]:
+ """Handles to create a training sample from a given file.
+
+ Args:
+ file (Path): The name of the file to be used for training
+ figure_of_merit (reward.reward_functions, optional): The figure of merit to be used for compilation. Defaults to "expected_fidelity".
+ path_uncompiled_circuit (Path): The path to the directory containing the uncompiled circuits. Defaults to None.
+ path_compiled_circuits (Path): The path to the directory containing the compiled circuits. Defaults to None.
+ logger_level (int, optional): The level of the logger. Defaults to logging.INFO.
+
+ Returns:
+ tuple[tuple[list[Any], Any], str, list[float]]: Training_sample, circuit_name, scores
"""
logger.setLevel(logger_level)
- if not path_uncompiled_circuit:
- path_uncompiled_circuit = str(ml.helper.get_path_training_circuits())
-
- if not path_compiled_circuits:
- path_compiled_circuits = str(ml.helper.get_path_training_circuits_compiled())
- if ".qasm" not in file:
- return False
+ if ".qasm" not in str(file):
+ raise RuntimeError("File is not a qasm file: " + str(file))
- LUT = ml.helper.get_index_to_comppath_LUT()
- logger.debug("Checking " + file)
+ LUT = ml.helper.get_index_to_device_LUT()
+ logger.debug("Checking " + str(file))
scores: list[float] = []
for _ in range(len(LUT)):
- scores.append(ml.helper.get_width_penalty())
- all_relevant_files = Path(path_compiled_circuits).glob(file.split(".")[0] + "*")
+ scores.append(-1.0)
+ all_relevant_files = path_compiled_circuits.glob(str(file).split(".")[0] + "*")
for filename in all_relevant_files:
filename_str = str(filename)
- if (file.split(".")[0] + "_") in filename_str and filename_str.endswith(".qasm"):
- comp_path_index = int(filename_str.split("_")[-1].split(".")[0])
- device = LUT[comp_path_index][1]
-
- score = reward.expected_fidelity(filename_str, device)
- scores[comp_path_index] = score
+ if (str(file).split(".")[0] + "_" + figure_of_merit + "_") not in filename_str and filename_str.endswith(
+ ".qasm"
+ ):
+ continue
+ comp_path_index = int(filename_str.split("_")[-1].split(".")[0])
+ device = LUT[comp_path_index]
+ qc = QuantumCircuit.from_qasm_file(filename_str)
+ if figure_of_merit == "critical_depth":
+ score = reward.crit_depth(qc)
+ elif figure_of_merit == "expected_fidelity":
+ score = reward.expected_fidelity(qc, device)
+ scores[comp_path_index] = score
num_not_empty_entries = 0
for i in range(len(LUT)):
- if scores[i] != ml.helper.get_width_penalty():
+ if scores[i] != -1.0:
num_not_empty_entries += 1
if num_not_empty_entries == 0:
- return False
+ logger.warning("no compiled circuits found for:" + str(file))
- feature_vec = ml.helper.create_feature_dict(str(Path(path_uncompiled_circuit) / file))
+ feature_vec = ml.helper.create_feature_dict(str(path_uncompiled_circuit / file))
training_sample = (list(feature_vec.values()), np.argmax(scores))
- circuit_name = file.split(".")[0]
-
+ circuit_name = str(file).split(".")[0]
return (training_sample, circuit_name, scores)
- def train_random_forest_classifier(self, visualize_results: bool = False) -> bool:
- (
- X_train,
- X_test,
- y_train,
- y_test,
- indices_train,
- indices_test,
- names_list,
- scores_list,
- ) = self.get_prepared_training_data(save_non_zero_indices=True)
+ def train_random_forest_classifier(
+ self, figure_of_merit: reward.figure_of_merit = "expected_fidelity", visualize_results: bool = False
+ ) -> bool:
+ """Trains a random forest classifier for the given figure of merit.
+
+ Args:
+ figure_of_merit (reward.reward_functions, optional): The figure of merit to be used for training. Defaults to "expected_fidelity".
+ visualize_results (bool, optional): Whether to visualize the results. Defaults to False.
+
+ Returns:
+ bool: Whether the training was successful.
+ """
+
+ training_data = self.get_prepared_training_data(figure_of_merit, save_non_zero_indices=True)
- scores_filtered = [scores_list[i] for i in indices_test]
- names_filtered = [names_list[i] for i in indices_test]
+ scores_filtered = [training_data.scores_list[i] for i in training_data.indices_test]
+ names_filtered = [training_data.names_list[i] for i in training_data.indices_test]
tree_param = [
{
@@ -299,37 +329,48 @@ def train_random_forest_classifier(self, visualize_results: bool = False) -> boo
]
clf = RandomForestClassifier(random_state=0)
- clf = GridSearchCV(clf, tree_param, cv=5, n_jobs=8).fit(X_train, y_train)
+ clf = GridSearchCV(clf, tree_param, cv=5, n_jobs=8).fit(training_data.X_train, training_data.y_train)
if visualize_results:
- y_pred = np.array(list(clf.predict(X_test)))
- res, _ = self.calc_performance_measures(scores_filtered, y_pred, y_test)
+ y_pred = np.array(list(clf.predict(training_data.X_test)))
+ res, _ = self.calc_performance_measures(scores_filtered, y_pred, training_data.y_test)
self.plot_eval_histogram(res, filename="RandomForestClassifier")
logger.info("Best Accuracy: " + str(clf.best_score_))
top3 = (res.count(1) + res.count(2) + res.count(3)) / len(res)
logger.info("Top 3: " + str(top3))
logger.info("Feature Importance: " + str(clf.best_estimator_.feature_importances_))
- self.plot_eval_all_detailed_compact_normed(names_filtered, scores_filtered, y_pred, y_test)
+ self.plot_eval_all_detailed_compact_normed(names_filtered, scores_filtered, y_pred, training_data.y_test)
self.set_classifier(clf.best_estimator_)
- ml.helper.save_classifier(clf.best_estimator_)
+ ml.helper.save_classifier(clf.best_estimator_, figure_of_merit)
logger.info("Random Forest classifier is trained and saved.")
return self.clf is not None
def get_prepared_training_data(
- self, save_non_zero_indices: bool = False
- ) -> tuple[Any, Any, Any, Any, Any, Any, Any, Any]:
- training_data, names_list, scores_list = ml.helper.load_training_data()
- X, y = zip(*training_data)
- X = list(X)
- y = list(y)
- for i in range(len(X)):
- X[i] = list(X[i])
- scores_list[i] = list(scores_list[i])
-
- X, y, indices = np.array(X), np.array(y), np.array(range(len(y)))
+ self, figure_of_merit: reward.figure_of_merit, save_non_zero_indices: bool = False
+ ) -> ml.helper.TrainingData:
+ """Prepares the training data for the given figure of merit.
+
+ Args:
+ figure_of_merit (reward.reward_functions): The figure of merit to be used for training.
+ save_non_zero_indices (bool, optional): Whether to save the non zero indices. Defaults to False.
+
+ Returns:
+ ml.helper.TrainingData: The prepared training data.
+ """
+ training_data, names_list, raw_scores_list = ml.helper.load_training_data(figure_of_merit)
+ unzipped_training_data_X, unzipped_training_data_Y = zip(*training_data)
+ scores_list: list[list[float]] = [[] for _ in range(len(raw_scores_list))]
+ X_raw = list(unzipped_training_data_X)
+ X_list: list[list[float]] = [[] for _ in range(len(X_raw))]
+ y_list = list(unzipped_training_data_Y)
+ for i in range(len(X_raw)):
+ X_list[i] = list(X_raw[i])
+ scores_list[i] = list(raw_scores_list[i])
+
+ X, y, indices = np.array(X_list), np.array(y_list), np.array(range(len(y_list)))
# Store all non zero feature indices
non_zero_indices = []
@@ -340,7 +381,10 @@ def get_prepared_training_data(
if save_non_zero_indices:
data = np.asarray(non_zero_indices)
- np.save(ml.helper.get_path_trained_model() / "non_zero_indices.npy", data)
+ np.save(
+ ml.helper.get_path_trained_model(figure_of_merit, return_non_zero_indices=True),
+ data,
+ )
(
X_train,
@@ -351,7 +395,7 @@ def get_prepared_training_data(
indices_test,
) = train_test_split(X, y, indices, test_size=0.3, random_state=5)
- return (
+ return ml.helper.TrainingData(
X_train,
X_test,
y_train,
@@ -370,15 +414,13 @@ def calc_performance_measures(
) -> tuple[list[int], list[float]]:
"""Method to generate the performance measures for a trained classifier
- Keyword arguments:
- scores_filtered -- ground truth of all combinations of compilation options
- y_pred -- predicted combination of compilation options
- y_test -- best combination of compilation options
-
- Return values:
- res -- list of all ranks
- relative_scores -- performance difference to best score
+ Args:
+ scores_filtered (list[list[float]]): The scores filtered for the respectively predicted indices of all training data
+ y_pred (np.ndarray[Any, np.dtype[np.float64]]): The predicted labels
+ y_test (np.ndarray[Any, np.dtype[np.float64]]): The actual labels
+ Returns:
+ tuple[list[int], list[float]]: The ranks and the relative scores
"""
res = []
@@ -386,8 +428,7 @@ def calc_performance_measures(
for i in range(len(y_pred)):
assert np.argmax(scores_filtered[i]) == y_test[i]
predicted_score = scores_filtered[i][y_pred[i]]
- tmp_predicted_score = 0 if predicted_score == ml.helper.get_width_penalty() else predicted_score
- relative_scores.append(tmp_predicted_score - np.max(scores_filtered[i]))
+ relative_scores.append(predicted_score - np.max(scores_filtered[i]))
score = list(np.sort(scores_filtered[i])[::-1]).index(predicted_score)
res.append(score + 1)
@@ -396,16 +437,18 @@ def calc_performance_measures(
return res, relative_scores
def plot_eval_histogram(self, res: list[int], filename: str = "histogram", color: str = "#21918c") -> None:
- """Method to generate the histogram of the resulting ranks
+ """Method to generate the histogram for the evaluation scores
+
+ Args:
+ res (list[int]): The ranks of the predictions
+ filename (str, optional): The filename of the histogram. Defaults to "histogram".
+ color (str, optional): The color of the histogram. Defaults to "#21918c".
- Keyword arguments:
- res -- all ranks as a list
- filename -- name of the file to save the histogram
"""
plt.figure(figsize=(10, 5))
- num_of_comp_paths = len(ml.helper.get_index_to_comppath_LUT())
+ num_of_comp_paths = len(ml.helper.get_index_to_device_LUT())
plt.bar(
list(range(0, num_of_comp_paths, 1)),
height=[res.count(i) / len(res) for i in range(1, num_of_comp_paths + 1, 1)],
@@ -441,13 +484,16 @@ def plot_eval_all_detailed_compact_normed(
color_all: str = "#21918c",
color_pred: str = "#440154",
) -> None:
- """Method to generate the detailed graph to examine the differences in evaluation scores
+ """Method to generate the plot for the evaluation scores of all training data
+
+ Args:
+ names_list (list[Any]): The names of all training data
+ scores_filtered (list[Any]): The scores filtered for the respectively predicted indices of all training data
+ y_pred (np.ndarray[Any, np.dtype[np.float64]]): The predicted labels
+ y_test (np.ndarray[Any, np.dtype[np.float64]]): The actual labels
+ color_all (str, optional): The color of the evaluation scores of all training data. Defaults to "#21918c".
+ color_pred (str, optional): The color of the evaluation scores of the predicted training data. Defaults to "#440154".
- Keyword arguments:
- names_list -- all names filtered for the respectively predicted indices of all training data
- scores_filtered -- all scores filtered for the respectively predicted indices of all training data
- y_pred -- predicted labels
- y_test -- actual labels
"""
# Create list of all qubit numbers and sort them
@@ -497,11 +543,19 @@ def plot_eval_all_detailed_compact_normed(
result_path.mkdir()
plt.savefig(result_path / "y_pred_eval_normed.pdf", bbox_inches="tight")
- def predict(self, qasm_str_or_path: str | QuantumCircuit) -> int:
- """Returns a compilation option prediction index for a given qasm file path or qasm string."""
+ def predict_probs(self, qasm_str_or_path: str | QuantumCircuit, figure_of_merit: reward.figure_of_merit) -> int:
+ """Returns the probabilities for all supported quantum devices to be the most suitable one for the given quantum circuit.
+
+ Args:
+ qasm_str_or_path (str | QuantumCircuit): The qasm string or path to the qasm file
+ figure_of_merit (reward.reward_functions): The figure of merit to be used for prediction
+
+ Returns:
+ int: The index of the predicted compilation option
+ """
if self.clf is None:
- path = ml.helper.get_path_trained_model() / "trained_clf.joblib"
+ path = ml.helper.get_path_trained_model(figure_of_merit)
if path.is_file():
self.clf = load(str(path))
else:
@@ -511,66 +565,8 @@ def predict(self, qasm_str_or_path: str | QuantumCircuit) -> int:
feature_dict = ml.helper.create_feature_dict(qasm_str_or_path)
feature_vector = list(feature_dict.values())
- path = ml.helper.get_path_trained_model() / "non_zero_indices.npy"
- non_zero_indices = np.load(str(path), allow_pickle=True)
+ path = ml.helper.get_path_trained_model(figure_of_merit, return_non_zero_indices=True)
+ non_zero_indices = np.load(path, allow_pickle=True)
feature_vector = [feature_vector[i] for i in non_zero_indices]
- return cast(int, self.clf.predict([feature_vector])[0]) # type: ignore[attr-defined]
-
- def compile_as_predicted(self, qc: str | QuantumCircuit, prediction: int) -> tuple[QuantumCircuit, int]:
- """Returns the compiled quantum circuit when the original qasm circuit is provided as either
- a string or a file path and the prediction index is given."""
- LUT = ml.helper.get_index_to_comppath_LUT()
- if prediction < 0 or prediction >= len(LUT):
- error_msg = "Prediction index is out of range."
- raise IndexError(error_msg)
- if not isinstance(qc, QuantumCircuit):
- if Path(qc).exists():
- logger.info("Reading from .qasm path: " + str(qc))
- qc = QuantumCircuit.from_qasm_file(qc)
- elif QuantumCircuit.from_qasm_str(qc):
- logger.info("Reading from .qasm str")
- qc = QuantumCircuit.from_qasm_str(qc)
- else:
- error_msg = "Invalid 'qc' parameter value."
- raise ValueError(error_msg)
-
- prediction_information = LUT[prediction]
- gate_set_name = prediction_information[0]
- device = prediction_information[1]
- compiler = prediction_information[2]
- compiler_settings = prediction_information[3]
-
- if compiler == "qiskit":
- compiled_qc = qiskit_helper.get_mapped_level(
- qc, gate_set_name, qc.num_qubits, device, compiler_settings, False, True
- )
- return compiled_qc, ml.helper.get_index_to_comppath_LUT()[prediction]
- if compiler == "tket":
- compiled_qc = tket_helper.get_mapped_level(
- qc,
- gate_set_name,
- qc.num_qubits,
- device,
- compiler_settings,
- False,
- True,
- )
- return (
- tk_to_qiskit(compiled_qc),
- ml.helper.get_index_to_comppath_LUT()[prediction],
- )
- error_msg = "Invalid compiler name."
- raise ValueError(error_msg)
-
- def instantiate_supervised_ML_model(self, timeout: int) -> None:
- # Generate compiled circuits and save them as qasm files
- self.generate_compiled_circuits(
- timeout=timeout,
- )
- # Generate training data from qasm files
- res = self.generate_trainingdata_from_qasm_files()
- # Save those training data for faster re-processing
- ml.helper.save_training_data(res)
- # Train the Random Forest Classifier on created training data
- self.train_random_forest_classifier()
+ return cast(int, self.clf.predict_proba([feature_vector])[0]) # type: ignore[attr-defined]
diff --git a/src/mqt/predictor/ml/__init__.py b/src/mqt/predictor/ml/__init__.py
index 7eefee805..dfa036e0f 100644
--- a/src/mqt/predictor/ml/__init__.py
+++ b/src/mqt/predictor/ml/__init__.py
@@ -1,10 +1,7 @@
-from mqt.predictor.ml import helper
from mqt.predictor.ml.helper import qcompile
from mqt.predictor.ml.Predictor import Predictor
-
__all__ = [
- "helper",
"qcompile",
"Predictor",
]
diff --git a/src/mqt/predictor/ml/helper.py b/src/mqt/predictor/ml/helper.py
index 2ec2d1cdf..1303af4bc 100644
--- a/src/mqt/predictor/ml/helper.py
+++ b/src/mqt/predictor/ml/helper.py
@@ -8,95 +8,100 @@
else:
from importlib import resources # type: ignore[no-redef]
+from dataclasses import dataclass
from pathlib import Path
from typing import TYPE_CHECKING
import numpy as np
from joblib import dump
from mqt.bench.utils import calc_supermarq_features
-from mqt.predictor import ml
+from mqt.predictor import ml, reward, rl
from qiskit import QuantumCircuit
if TYPE_CHECKING:
+ from numpy._typing import NDArray
from sklearn.ensemble import RandomForestClassifier
-def qcompile(qc: QuantumCircuit | str) -> tuple[QuantumCircuit, int]:
- """Returns the compiled quantum circuit which is compiled with the predicted combination of compilation options.
+def qcompile(
+ qc: QuantumCircuit, figure_of_merit: reward.figure_of_merit = "expected_fidelity"
+) -> tuple[QuantumCircuit, list[str], str] | bool:
+ """Compiles a given quantum circuit to a device with the highest predicted figure of merit.
- Keyword arguments:
- qc -- to be compiled quantum circuit or path to a qasm file
+ Args:
+ qc (QuantumCircuit): The quantum circuit to be compiled.
+ figure_of_merit (reward.reward_functions, optional): The figure of merit to be used for compilation. Defaults to "expected_fidelity".
- Returns: compiled quantum circuit as Qiskit QuantumCircuit object
+ Returns:
+ tuple[QuantumCircuit, list[str], str] | bool: Returns a tuple containing the compiled quantum circuit, the compilation information and the name of the device used for compilation. If compilation fails, False is returned.
"""
- predictor = ml.Predictor()
- prediction = predictor.predict(qc)
- return predictor.compile_as_predicted(qc, prediction)
+ device_name = predict_device_for_figure_of_merit(qc, figure_of_merit)
+ assert device_name is not None
+ res = rl.qcompile(qc, figure_of_merit=figure_of_merit, device_name=device_name)
+ return *res, device_name
+
+
+def predict_device_for_figure_of_merit(
+ qc: QuantumCircuit, figure_of_merit: reward.figure_of_merit = "expected_fidelity"
+) -> str:
+ """Returns the name of the device with the highest predicted figure of merit that is suitable for the given quantum circuit.
+
+ Args:
+ qc (QuantumCircuit): The quantum circuit to be compiled.
+ figure_of_merit (reward.reward_functions, optional): The figure of merit to be used for compilation. Defaults to "expected_fidelity".
+
+ Returns:
+ str : The name of the device with the highest predicted figure of merit that is suitable for the given quantum circuit.
+ """
+
+ ml_predictor = ml.Predictor()
+ predicted_device_index_probs = ml_predictor.predict_probs(qc, figure_of_merit)
+ assert ml_predictor.clf is not None
+ classes = ml_predictor.clf.classes_ # type: ignore[unreachable]
+ predicted_device_index = classes[np.argsort(predicted_device_index_probs)[::-1]]
+ devices = rl.helper.get_devices()
+
+ for index in predicted_device_index:
+ if devices[index]["max_qubits"] >= qc.num_qubits:
+ return devices[index]["name"]
+ msg = "No suitable device found."
+ raise ValueError(msg)
def get_path_training_data() -> Path:
+ """Returns the path to the training data folder."""
return Path(str(resources.files("mqt.predictor"))) / "ml" / "training_data"
-def get_path_trained_model() -> Path:
- return get_path_training_data() / "trained_model"
+def get_path_results(ghz_results: bool = False) -> Path:
+ """Returns the path to the results file."""
+ if ghz_results:
+ return get_path_training_data() / "trained_model" / "res_GHZ.csv"
+ return get_path_training_data() / "trained_model" / "res.csv"
+
+
+def get_path_trained_model(figure_of_merit: str, return_non_zero_indices: bool = False) -> Path:
+ """Returns the path to the trained model folder resulting from the machine learning training."""
+ if return_non_zero_indices:
+ return get_path_training_data() / "trained_model" / ("non_zero_indices_" + figure_of_merit + ".npy")
+ return get_path_training_data() / "trained_model" / ("trained_clf_" + figure_of_merit + ".joblib")
def get_path_training_circuits() -> Path:
+ """Returns the path to the training circuits folder."""
return get_path_training_data() / "training_circuits"
def get_path_training_circuits_compiled() -> Path:
+ """Returns the path to the compiled training circuits folder."""
return get_path_training_data() / "training_circuits_compiled"
-def get_width_penalty() -> int:
- """Returns the penalty value if a quantum computer has not enough qubits."""
- return -10000
-
-
-def get_compilation_pipeline() -> dict[str, dict[str, Any]]:
- return {
- "devices": {
- "ibm": [("ibm_washington", 127), ("ibm_montreal", 27)],
- "rigetti": [("rigetti_aspen_m2", 80)],
- "ionq": [("ionq11", 11)],
- "oqc": [("oqc_lucy", 8)],
- },
- "compiler": {
- "qiskit": {"optimization_level": [0, 1, 2, 3]},
- "tket": {"lineplacement": [False, True]},
- },
- }
-
-
-def get_index_to_comppath_LUT() -> dict[int, Any]:
- compilation_pipeline = get_compilation_pipeline()
- index = 0
- index_to_comppath_LUT = {}
- for gate_set_name, devices in compilation_pipeline["devices"].items():
- for device_name, _max_qubits in devices:
- for compiler, settings in compilation_pipeline["compiler"].items():
- if "qiskit" in compiler:
- for opt_level in settings["optimization_level"]:
- index_to_comppath_LUT[index] = (
- gate_set_name,
- device_name,
- compiler,
- opt_level,
- )
- index += 1
- elif "tket" in compiler:
- for lineplacement in settings["lineplacement"]:
- index_to_comppath_LUT[index] = (
- gate_set_name,
- device_name,
- compiler,
- lineplacement,
- )
- index += 1
- return index_to_comppath_LUT
+def get_index_to_device_LUT() -> dict[int, str]:
+ """Returns a look-up table (LUT) that maps the index of a device to its name."""
+ devices = rl.helper.get_devices()
+ return {i: device["name"] for i, device in enumerate(devices)}
def get_openqasm_gates() -> list[str]:
@@ -162,6 +167,14 @@ def dict_to_featurevector(gate_dict: dict[str, int]) -> dict[str, int]:
def create_feature_dict(qc: str | QuantumCircuit) -> dict[str, Any]:
+ """Creates and returns a feature dictionary for a given quantum circuit.
+
+ Args:
+ qc (str | QuantumCircuit): The quantum circuit to be compiled.
+
+ Returns:
+ dict[str, Any]: The feature dictionary of the given quantum circuit.
+ """
if not isinstance(qc, QuantumCircuit):
if len(qc) < PATH_LENGTH and Path(qc).exists():
qc = QuantumCircuit.from_qasm_file(qc)
@@ -190,34 +203,72 @@ def create_feature_dict(qc: str | QuantumCircuit) -> dict[str, Any]:
return feature_dict
-def save_classifier(clf: RandomForestClassifier) -> None:
- dump(clf, str(get_path_trained_model() / "trained_clf.joblib"))
+def save_classifier(clf: RandomForestClassifier, figure_of_merit: reward.figure_of_merit = "expected_fidelity") -> None:
+ """Saves the given classifier to the trained model folder.
+
+ Args:
+ clf (RandomForestClassifier): The classifier to be saved.
+ figure_of_merit (reward.reward_functions, optional): The figure of merit to be used for compilation. Defaults to "expected_fidelity".
+ """
+ dump(clf, str(get_path_trained_model(figure_of_merit)))
-def save_training_data(res: tuple[list[Any], list[Any], list[Any]]) -> None:
- training_data, names_list, scores_list = res
+def save_training_data(
+ training_data: list[NDArray[np.float_]],
+ names_list: list[str],
+ scores_list: list[NDArray[np.float_]],
+ figure_of_merit: reward.figure_of_merit,
+) -> None:
+ """Saves the given training data to the training data folder.
+
+ Args:
+ res (tuple[list[Any], list[Any], list[Any]]): The training data, the names list and the scores list to be saved.
+ figure_of_merit (reward.reward_functions, optional): The figure of merit to be used for compilation. Defaults to "expected_fidelity".
+ """
with resources.as_file(get_path_training_data() / "training_data_aggregated") as path:
data = np.asarray(training_data, dtype=object)
- np.save(str(path / "training_data.npy"), data)
+ np.save(str(path / ("training_data_" + figure_of_merit + ".npy")), data)
data = np.asarray(names_list)
- np.save(str(path / "names_list.npy"), data)
+ np.save(str(path / ("names_list_" + figure_of_merit + ".npy")), data)
data = np.asarray(scores_list)
- np.save(str(path / "scores_list.npy"), data)
+ np.save(str(path / ("scores_list_" + figure_of_merit + ".npy")), data)
-def load_training_data() -> tuple[list[Any], list[str], list[Any]]:
+def load_training_data(
+ figure_of_merit: reward.figure_of_merit = "expected_fidelity",
+) -> tuple[NDArray[np.float_], list[str], list[NDArray[np.float_]]]:
+ """Loads and returns the training data from the training data folder.
+
+ Args:
+ figure_of_merit (reward.reward_functions, optional): The figure of merit to be used for compilation. Defaults to "expected_fidelity".
+
+ Returns:
+ tuple[NDArray[np.float_], list[str], list[NDArray[np.float_]]]: The training data, the names list and the scores list.
+ """
with resources.as_file(get_path_training_data() / "training_data_aggregated") as path:
if (
- path.joinpath("training_data.npy").is_file()
- and path.joinpath("names_list.npy").is_file()
- and path.joinpath("scores_list.npy").is_file()
+ path.joinpath("training_data_" + figure_of_merit + ".npy").is_file()
+ and path.joinpath("names_list_" + figure_of_merit + ".npy").is_file()
+ and path.joinpath("scores_list_" + figure_of_merit + ".npy").is_file()
):
- training_data = np.load(str(path / "training_data.npy"), allow_pickle=True)
- names_list = list(np.load(str(path / "names_list.npy"), allow_pickle=True))
- scores_list = list(np.load(str(path / "scores_list.npy"), allow_pickle=True))
+ training_data = np.load(path / ("training_data_" + figure_of_merit + ".npy"), allow_pickle=True)
+ names_list = list(np.load(path / ("names_list_" + figure_of_merit + ".npy"), allow_pickle=True))
+ scores_list = list(np.load(path / ("scores_list_" + figure_of_merit + ".npy"), allow_pickle=True))
else:
error_msg = "Training data not found. Please run the training script first."
raise FileNotFoundError(error_msg)
return training_data, names_list, scores_list
+
+
+@dataclass
+class TrainingData:
+ X_train: NDArray[np.float_]
+ X_test: NDArray[np.float_]
+ y_train: NDArray[np.float_]
+ y_test: NDArray[np.float_]
+ indices_train: list[int]
+ indices_test: list[int]
+ names_list: list[str]
+ scores_list: list[list[float]]
diff --git a/src/mqt/predictor/ml/training_data/ghz/training_data_ghz.zip b/src/mqt/predictor/ml/training_data/ghz/training_data_ghz.zip
new file mode 100644
index 000000000..24ecc9f42
Binary files /dev/null and b/src/mqt/predictor/ml/training_data/ghz/training_data_ghz.zip differ
diff --git a/src/mqt/predictor/ml/training_data/trained_model/non_zero_indices.npy b/src/mqt/predictor/ml/training_data/trained_model/non_zero_indices.npy
deleted file mode 100644
index 3720ff6df..000000000
Binary files a/src/mqt/predictor/ml/training_data/trained_model/non_zero_indices.npy and /dev/null differ
diff --git a/src/mqt/predictor/ml/training_data/trained_model/non_zero_indices_critical_depth.npy b/src/mqt/predictor/ml/training_data/trained_model/non_zero_indices_critical_depth.npy
new file mode 100644
index 000000000..318137187
Binary files /dev/null and b/src/mqt/predictor/ml/training_data/trained_model/non_zero_indices_critical_depth.npy differ
diff --git a/src/mqt/predictor/ml/training_data/trained_model/non_zero_indices_expected_fidelity.npy b/src/mqt/predictor/ml/training_data/trained_model/non_zero_indices_expected_fidelity.npy
new file mode 100644
index 000000000..318137187
Binary files /dev/null and b/src/mqt/predictor/ml/training_data/trained_model/non_zero_indices_expected_fidelity.npy differ
diff --git a/src/mqt/predictor/ml/training_data/trained_model/res.csv b/src/mqt/predictor/ml/training_data/trained_model/res.csv
new file mode 100644
index 000000000..a26187a1b
--- /dev/null
+++ b/src/mqt/predictor/ml/training_data/trained_model/res.csv
@@ -0,0 +1,601 @@
+file_path,benchmark_name,num_qubits,MQTPredictor_expected_fidelity_time,MQTPredictor_expected_fidelity_expected_fidelity,MQTPredictor_expected_fidelity_critical_depth,MQTPredictor_critical_depth_time,MQTPredictor_critical_depth_expected_fidelity,MQTPredictor_critical_depth_critical_depth,qiskit_ibm_washington_time,qiskit_ibm_washington_expected_fidelity,qiskit_ibm_washington_critical_depth,qiskit_ibm_montreal_time,qiskit_ibm_montreal_expected_fidelity,qiskit_ibm_montreal_critical_depth,qiskit_oqc_lucy_time,qiskit_oqc_lucy_expected_fidelity,qiskit_oqc_lucy_critical_depth,qiskit_rigetti_aspen_m2_time,qiskit_rigetti_aspen_m2_expected_fidelity,qiskit_rigetti_aspen_m2_critical_depth,qiskit_ionq_harmony_time,qiskit_ionq_harmony_expected_fidelity,qiskit_ionq_harmony_critical_depth,qiskit_ionq_aria1_time,qiskit_ionq_aria1_expected_fidelity,qiskit_ionq_aria1_critical_depth,qiskit_quantinuum_h2_time,qiskit_quantinuum_h2_expected_fidelity,qiskit_quantinuum_h2_critical_depth,tket_ibm_washington_time,tket_ibm_washington_expected_fidelity,tket_ibm_washington_critical_depth,tket_ibm_montreal_time,tket_ibm_montreal_expected_fidelity,tket_ibm_montreal_critical_depth,tket_oqc_lucy_time,tket_oqc_lucy_expected_fidelity,tket_oqc_lucy_critical_depth,tket_rigetti_aspen_m2_time,tket_rigetti_aspen_m2_expected_fidelity,tket_rigetti_aspen_m2_critical_depth,tket_ionq_harmony_time,tket_ionq_harmony_expected_fidelity,tket_ionq_harmony_critical_depth,tket_ionq_aria1_time,tket_ionq_aria1_expected_fidelity,tket_ionq_aria1_critical_depth,tket_quantinuum_h2_time,tket_quantinuum_h2_expected_fidelity,tket_quantinuum_h2_critical_depth
+qft_indep_tket_8,qft,8,-1,0.6815428369,0.5735294118,-1,0.0310686159,0.5735294118,0.20340490341186523,0.2003045673,0.373015873,0.18787884712219238,0.3076660169,0.3740458015,12.04861307144165,1.66846e-05,0.4390243902,1.655573844909668,4e-10,0.3211009174,93.86258006095886,0.0531639941,0.5735294118,7.486894845962524,0.4973259978,0.5735294118,7.535559892654419,0.6815428369,0.5735294118,1.6769638061523438,0.2453045899,0.375,1.3838939666748047,0.2243075859,0.3893129771,1.169389009475708,9.6472e-06,0.5071428571,1.3054561614990234,5.5e-09,0.2131147541,1.1567091941833496,0.0334104311,0.5357142857,1.5239770412445068,0.4842150815,0.5357142857,1.3263628482818604,0.5866359128,0.5357142857
+graphstate_indep_tket_28,graphstate,28,-1,0.8692241288,0.6785714286,-1,0.0,0.7537313433,0.31668806076049805,0.1240051324,0.7142857143,-1,-1,-1,-1,-1,-1,0.7737400531768799,1.9571e-06,0.6883116883,-1,-1,-1,-1,-1,-1,19.04753589630127,0.8692241288,0.6785714286,1.7617616653442383,0.0432326821,0.6972477064,-1,-1,-1,-1,-1,-1,1.9477989673614502,2e-10,0.5984251969,-1,-1,-1,-1,-1,-1,1.415414810180664,0.5382705678,0.6785714286
+wstate_indep_qiskit_26,wstate,26,-1,0.702347475,0.46,-1,0.0,0.6018099548,4.0169737339019775,0.1804545709,0.46,0.4137401580810547,0.2101064843,0.3625,-1,-1,-1,11.23494029045105,1.78189e-05,0.46,-1,-1,-1,-1,-1,-1,10.664260864257812,0.702347475,0.46,1.9489731788635254,0.1969485277,0.46,3.139482259750366,0.1069706955,0.3984375,-1,-1,-1,1.8080289363861084,2.963e-07,0.46,-1,-1,-1,-1,-1,-1,1.7883739471435547,0.3750707036,0.46
+graphstate_indep_qiskit_30,graphstate,30,-1,0.8605657212,0.8,-1,0.0,0.811023622,0.32034993171691895,0.0525377532,0.6727272727,-1,-1,-1,-1,-1,-1,2.8181278705596924,1.4553e-06,0.7846153846,-1,-1,-1,-1,-1,-1,24.2939670085907,0.8605657212,0.8,2.2154998779296875,0.0132922292,0.6622222222,-1,-1,-1,-1,-1,-1,2.7154958248138428,9.2e-09,0.6868686869,-1,-1,-1,-1,-1,-1,1.3490979671478271,0.5192250024,0.8
+portfolioqaoa_indep_tket_7,portfolioqaoa,7,-1,0.7769994867,0.6031746032,-1,0.0224184387,0.6031746032,2.4185938835144043,0.1134777295,0.3856502242,0.4248178005218506,0.1151682124,0.3162393162,14.928505659103394,4.2e-09,0.4263565891,2.528075933456421,0.0,0.3551401869,91.80106210708618,0.0855337594,0.6031746032,26.965261936187744,0.5447176633,0.6031746032,6.340155839920044,0.7769994867,0.6031746032,2.467829942703247,0.0007152332,0.4471544715,2.368748903274536,0.0104725288,0.3601532567,2.5668599605560303,0.0,0.5769230769,2.6109910011291504,0.0,0.4183673469,2.203369140625,0.0005310181,0.6031746032,2.268502950668335,0.198800109,0.6031746032,1.9722816944122314,0.3266882659,0.6031746032
+qaoa_indep_qiskit_6,qaoa,6,-1,0.9360661628,0.5833333333,-1,0.0059615495,0.6137566138,0.10246109962463379,0.2815652676,0.3846153846,0.10151386260986328,0.5322599501,0.3846153846,0.5333759784698486,0.0333619791,0.6111111111,0.14406609535217285,0.005417992,0.5,80.82201409339905,0.6066543237,0.5833333333,7.40499210357666,0.8864656546,0.5833333333,4.161659002304077,0.9360661628,0.5833333333,0.6774868965148926,0.1577483825,0.4126984127,0.6378769874572754,0.4457715649,0.6031746032,0.5332417488098145,0.02752623,0.5128205128,0.6635940074920654,0.0297326373,0.3939393939,2.751514196395874,0.2254609845,0.5833333333,0.6442780494689941,0.7291755564,0.5833333333,0.49805593490600586,0.7952087083,0.5833333333
+dj_indep_tket_25,dj,25,-1,0.8334652635,0.0,-1,0.0,0.7061728395,0.4165358543395996,0.0870326927,0.0675675676,2.1546900272369385,0.290607182,0.1176470588,-1,-1,-1,3.6001029014587402,2.9436e-06,0.0298507463,-1,-1,-1,0.3414037227630615,0.7786235215,0.0,71.11495804786682,0.8334652635,0.0,1.5705139636993408,0.1293156035,0.3963963964,1.5043787956237793,0.0508521925,0.4619047619,-1,-1,-1,1.4194531440734863,0.0,0.3137254902,-1,-1,-1,0.7809140682220459,0.7127351979,0.0,0.883206844329834,0.6621399571,0.0
+graphstate_indep_tket_90,graphstate,90,-1,2.2192e-06,0.8795811518,-1,0.0,0.8691019787,27.40132212638855,3.54995e-05,0.8992628993,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,4.484502077102661,0.0009039993,0.8027210884,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1
+ae_indep_tket_30,ae,30,-1,0.0158169788,0.8055555556,-1,3.839e-07,0.8068965517,3.625943899154663,0.0,0.6526977475,-1,-1,-1,-1,-1,-1,13.841068029403687,0.0,0.653272101,-1,-1,-1,-1,-1,-1,106.03762197494507,0.0119403728,0.8068965517,20.120849132537842,0.0,0.6916666667,-1,-1,-1,-1,-1,-1,19.753355026245117,0.0,0.6301169591,-1,-1,-1,-1,-1,-1,14.218104124069214,0.0005961484,0.8068965517
+ae_indep_tket_7,ae,7,-1,0.7943418145,0.2857142857,-1,0.160528069,0.431372549,0.14888286590576172,0.1795321348,0.0810810811,0.1606309413909912,0.4016138058,0.0833333333,1.10123610496521,0.0005301392,0.2105263158,0.3208122253417969,3.8899e-06,0.1692307692,92.06272792816162,0.1482090917,0.2857142857,6.545130252838135,0.642107605,0.2857142857,6.402987957000732,0.7943418145,0.2857142857,0.9670121669769287,0.2542215741,0.2688172043,0.7724461555480957,0.1973637523,0.2444444444,1.0208709239959717,0.0004244634,0.2708333333,1.1756279468536377,5.2e-09,0.2179487179,0.7924773693084717,0.077180205,0.2857142857,0.964407205581665,0.5793665394,0.2857142857,1.4544782638549805,0.6755289272,0.2857142857
+wstate_indep_tket_7,wstate,7,-1,0.9166041079,0.3333333333,-1,0.7449049386,0.3333333333,0.9419131278991699,0.6141845216,0.3333333333,1.2658021450042725,0.759219897,0.3333333333,10.166636228561401,0.1901565144,0.3333333333,21.924651861190796,0.0505134828,0.3333333333,4.023404121398926,0.5726703341,0.3333333333,4.073047876358032,0.8786974334,0.3333333333,4.084941148757935,0.9166041079,0.3333333333,0.3815929889678955,0.7644261679,0.3333333333,0.3490629196166992,0.674415681,0.3333333333,0.3489341735839844,0.1899819282,0.3333333333,0.36553502082824707,0.0367586924,0.3333333333,0.37355494499206543,0.4041541018,0.3333333333,0.46231865882873535,0.8331635644,0.3333333333,0.5509192943572998,0.7872923103,0.3333333333
+portfoliovqe_indep_qiskit_8,portfoliovqe,8,-1,0.7280312693,0.6547619048,-1,0.0072694569,0.6547619048,0.41440510749816895,0.0015569851,0.3625498008,0.4292149543762207,0.0834951391,0.3524904215,2.7606420516967773,4e-10,0.4907749077,0.6615931987762451,0.0,0.4123222749,7.514902114868164,0.038788387,0.6547619048,49.16052484512329,0.4468274949,0.6547619048,7.808228969573975,0.7280312693,0.6547619048,2.677805185317993,0.0009709346,0.4833333333,2.444227933883667,0.028216708,0.4833333333,2.173419713973999,0.0,0.6374269006,2.1172878742218018,0.0,0.619047619,2.0274581909179688,0.003927041,0.6547619048,2.065474033355713,0.3149779587,0.6547619048,2.251979112625122,0.2508389612,0.6547619048
+vqe_indep_qiskit_11,vqe,11,-1,0.8460780076,0.4,-1,0.043702606,0.6058394161,0.15314316749572754,0.380234808,0.4,0.0708160400390625,0.6791592969,0.4,-1,-1,-1,0.5775938034057617,0.0397598812,0.4,0.1290278434753418,0.3773827068,0.4,13.853628873825073,0.800547709,0.4,38.70544099807739,0.8460780076,0.4,0.6022801399230957,0.6254274554,0.4,0.5745282173156738,0.5803076854,0.4,-1,-1,-1,0.6438169479370117,0.0077166327,0.4,0.5753591060638428,0.206554949,0.4,0.5799291133880615,0.730170249,0.4,0.6387922763824463,0.6542457053,0.4
+qpeexact_indep_qiskit_15,qpeexact,15,-1,0.3534021898,0.6315789474,-1,2e-10,0.6744186047,0.6770360469818115,0.0003998009,0.4397163121,0.8088099956512451,0.0044242962,0.4784172662,-1,-1,-1,1.550534963607788,0.0,0.45,-1,-1,-1,43.317490100860596,0.0968070395,0.652173913,98.80099391937256,0.3026155016,0.6493506494,11.756659984588623,1.15222e-05,0.535483871,5.590075969696045,0.0021677473,0.4754990926,-1,-1,-1,5.846498012542725,0.0,0.5176252319,-1,-1,-1,4.158785820007324,0.0693737431,0.6315789474,3.64768385887146,0.1610196373,0.6285714286
+wstate_indep_tket_18,wstate,18,-1,0.7856665187,0.4411764706,-1,0.0005501804,0.5830618893,0.6529018878936768,0.2819028929,0.4411764706,0.09619879722595215,0.44356057,0.4411764706,-1,-1,-1,9.4845290184021,0.0009349828,0.4411764706,-1,-1,-1,18.18561100959778,0.6952681996,0.4411764706,45.51903796195984,0.7856665187,0.4411764706,1.0370798110961914,0.1754532795,0.4411764706,1.020902156829834,0.3997318223,0.4411764706,-1,-1,-1,1.0416250228881836,0.0001670415,0.4411764706,-1,-1,-1,0.9992129802703857,0.598404928,0.4411764706,1.102555751800537,0.5125092622,0.4411764706
+twolocalrandom_indep_qiskit_3,twolocalrandom,3,-1,0.9464334802,0.0,-1,0.0002447126,0.4202898551,1.2892239093780518,0.7629416391,0.0,1.025557041168213,0.8014665616,0.0,13.09623384475708,0.1594508636,0.0,0.32262182235717773,0.1756818081,0.0,0.04064321517944336,0.6678634357,0.0,0.32027101516723633,0.9095679677,0.0,0.7560739517211914,0.9464334802,0.0,0.28844714164733887,0.4050650143,0.0,0.1737070083618164,0.7023162072,0.0,0.17916107177734375,0.1148893894,0.0,0.29497194290161133,0.0556737218,0.0,0.1595900058746338,0.5152558328,0.0,0.1722121238708496,0.8742460029,0.0,0.20954394340515137,0.8376829166,0.0
+dj_indep_tket_5,dj,5,-1,0.9665434325,0.0,-1,0.3001902794,0.46875,0.33469200134277344,0.7449375753,0.0,0.4381890296936035,0.9006706526,0.0,0.04925894737243652,0.4010847869,0.0,0.05932974815368652,0.4258567073,0.0,0.7669620513916016,0.8311528619,0.0,3.427304267883301,0.9579386662,0.0,3.487421989440918,0.9665434325,0.0,0.37085390090942383,0.8858114952,0.0,0.23988103866577148,0.8790565347,0.0,0.14015412330627441,0.3002036366,0.0,0.3086400032043457,0.1220265801,0.0,0.1417248249053955,0.7543517056,0.0,0.16247010231018066,0.9438627772,0.0,0.18093085289001465,0.9304861792,0.0
+qpeexact_indep_qiskit_5,qpeexact,5,-1,0.8657576525,0.1923076923,-1,0.0025725119,0.119047619,1.3325393199920654,0.4556896688,0.075,0.0702979564666748,0.7354712824,0.0555555556,0.3558790683746338,0.0241738955,0.0555555556,0.15176606178283691,3.56459e-05,0.1428571429,1.013383150100708,0.3323684029,0.2,4.413336753845215,0.7708431403,0.2,4.4344518184661865,0.8657576525,0.1923076923,0.6439306735992432,0.5663216122,0.0,0.5791230201721191,0.7265450766,0.1176470588,0.45883965492248535,0.0171724196,0.1162790698,0.5037651062011719,0.0001198641,0.064516129,0.4051518440246582,0.309151718,0.1052631579,0.420306921005249,0.7794055464,0.1052631579,0.4252181053161621,0.8252027673,0.1
+realamprandom_indep_tket_4,realamprandom,4,-1,0.9056488079,0.2777777778,-1,0.4129794605,0.2608695652,1.4345488548278809,0.4269611715,0.0,1.1189959049224854,0.6568596526,0.0,0.4981367588043213,0.0063994505,0.2083333333,0.15202713012695312,0.0145949606,0.0,0.15803217887878418,0.4627087621,0.2777777778,3.613104820251465,0.8319607743,0.2777777778,3.6367127895355225,0.9056488079,0.2777777778,0.5393021106719971,0.2534812171,0.0,0.42964696884155273,0.6261643669,0.0,0.442310094833374,0.0040020039,0.2222222222,0.5698072910308838,1.58732e-05,0.1944444444,0.40578317642211914,0.2812434682,0.2777777778,0.4204981327056885,0.7710630055,0.2777777778,0.48748326301574707,0.7202058149,0.2777777778
+su2random_indep_qiskit_4,su2random,4,-1,0.9011296113,0.2777777778,-1,0.0190672982,0.3188405797,0.6597743034362793,0.4269611715,0.0,0.12512993812561035,0.6568596526,0.0,0.5089290142059326,0.0063312681,0.2083333333,0.3042418956756592,0.0142044464,0.0,0.17240500450134277,0.4390835511,0.2777777778,3.614091157913208,0.8253303191,0.2777777778,3.8793861865997314,0.9011296113,0.2777777778,0.542863130569458,0.2526484057,0.0,0.419299840927124,0.6241947626,0.0,0.43532323837280273,0.003859583,0.2222222222,0.5589280128479004,1.57299e-05,0.1944444444,0.4141368865966797,0.273252497,0.2777777778,0.4316847324371338,0.7676771055,0.2777777778,0.4952418804168701,0.7137498296,0.2777777778
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+dj_indep_tket_8,dj,8,-1,0.9445472421,0.0,-1,0.2099607148,0.4426229508,0.09038019180297852,0.6408504469,0.0,0.07083415985107422,0.6785624171,0.0,0.2107241153717041,0.119369024,0.0,0.09239482879638672,0.0689270557,0.0,4.427296161651611,0.7239792016,0.0,19.916125059127808,0.9276505774,0.0,4.619303941726685,0.9445472421,0.0,0.5732088088989258,0.527832462,0.0,0.4473438262939453,0.6198947374,0.2272727273,0.442471981048584,0.0768621491,0.04,0.5388526916503906,0.140734467,0.0,0.22310400009155273,0.6170301638,0.0,0.23480010032653809,0.905285395,0.0,0.26688385009765625,0.8850757036,0.0
+qpeinexact_indep_tket_15,qpeinexact,15,-1,0.3663619431,0.6285714286,-1,1e-10,0.6739659367,0.8361420631408691,0.0012811201,0.440227704,1.3599169254302979,0.0012172276,0.4990654206,-1,-1,-1,5.107403039932251,0.0,0.4613821138,-1,-1,-1,41.56024885177612,0.0959547966,0.6493506494,58.37909197807312,0.3029184201,0.6493506494,11.569287061691284,1.04959e-05,0.5346215781,5.315255165100098,0.0021409471,0.4746376812,-1,-1,-1,5.598572015762329,0.0,0.5166666667,-1,-1,-1,3.8598761558532715,0.0680786703,0.6285714286,3.3182671070098877,0.1568850418,0.6285714286
+dj_indep_tket_28,dj,28,-1,0.8144976103,0.0,-1,0.0021544893,0.4421768707,0.3175790309906006,0.1184568011,0.049382716,-1,-1,-1,-1,-1,-1,0.4451920986175537,4.542e-06,0.0147058824,-1,-1,-1,-1,-1,-1,10.942296266555786,0.8144976103,0.0,1.7310218811035156,0.0099235305,0.5601851852,-1,-1,-1,-1,-1,-1,1.774454116821289,0.0,0.3402777778,-1,-1,-1,-1,-1,-1,0.959404706954956,0.6298255702,0.0
+routing_indep_qiskit_2,routing,2,-1,0.973339309,0.0,-1,0.9508293848,0.0,0.034562110900878906,0.9251305142,0.0,0.02263188362121582,0.93967417,0.0,4.258386135101318,0.7792874271,0.0,0.09516787528991699,0.3164808033,0.0,0.023183822631835938,0.8463049475,0.0,0.032440900802612305,0.9641296472,0.0,0.031017065048217773,0.973339309,0.0,0.08109593391418457,0.9160040437,0.0,0.052825927734375,0.910781262,0.0,0.05141592025756836,0.5897068449,0.0,0.0642080307006836,0.5589780753,0.0,0.04865097999572754,0.817960852,0.0,0.060233116149902344,0.9591281878,0.0,0.08100581169128418,0.9636495995,0.0
+qft_indep_qiskit_6,qft,6,-1,0.7967368037,0.4615384615,-1,0.1361749733,0.4615384615,0.10301613807678223,0.3121793201,0.2258064516,0.09366893768310547,0.5235761601,0.2258064516,25.755128860473633,0.0047629145,0.2162162162,0.2339038848876953,0.0004499576,0.3272727273,5.368006944656372,0.1811933802,0.4615384615,6.9770708084106445,0.6673307522,0.4615384615,7.0729289054870605,0.7967368037,0.4615384615,0.8333609104156494,0.4185765782,0.2456140351,0.6405010223388672,0.3683093473,0.196969697,0.6589691638946533,0.0023608361,0.2727272727,0.8835349082946777,2.475e-07,0.2549019608,0.6437029838562012,0.1585473415,0.4,0.6465890407562256,0.6759066345,0.4,0.5648839473724365,0.743643728,0.4
+realamprandom_indep_qiskit_50,realamprandom,50,-1,-1,-1,-1,0.0,0.886108044,39.7647271156311,0.0,0.8472974303,-1,-1,-1,-1,-1,-1,48.608901023864746,0.0,0.8360739542,-1,-1,-1,-1,-1,-1,-1,-1,-1,119.77069807052612,0.0,0.7503505677,-1,-1,-1,-1,-1,-1,121.80730700492859,0.0,0.7974668969,-1,-1,-1,-1,-1,-1,-1,-1,-1
+dj_indep_qiskit_11,dj,11,-1,0.9239756075,0.0,-1,0.0172063251,0.4805194805,0.1270771026611328,0.5582713447,0.0416666667,0.05624222755432129,0.6112126247,0.0,-1,-1,-1,0.11990809440612793,0.0008020077,0.0434782609,0.03616905212402344,0.6322797941,0.0,5.838064908981323,0.8986796086,0.0,5.770761013031006,0.9239756075,0.0,0.7043910026550293,0.3966746869,0.0,0.6287622451782227,0.6532029252,0.0882352941,-1,-1,-1,0.6819756031036377,0.0002367093,0.1290322581,0.32798194885253906,0.5020683658,0.0,0.3386261463165283,0.8675902515,0.0,0.3831508159637451,0.8401984725,0.0
+graphstate_indep_tket_25,graphstate,25,-1,0.8823753539,0.8,-1,0.0001065775,0.8005540166,0.22072076797485352,0.1052785025,0.6914893617,0.11140918731689453,0.2620912519,0.775862069,-1,-1,-1,5.123508930206299,7.04477e-05,0.6981132075,-1,-1,-1,0.06845498085021973,0.7723866784,0.8,10.042682886123657,0.8823753539,0.8,1.7106642723083496,0.155772558,0.5961538462,2.013576030731201,0.0894067779,0.5521472393,-1,-1,-1,1.7713048458099365,4.24e-08,0.5409836066,-1,-1,-1,0.7978959083557129,0.6749633116,0.8,0.8706362247467041,0.588404656,0.8
+dj_indep_tket_90,dj,90,-1,5.83214e-05,0.2434607646,-1,0.0,0.4655568312,1.4174818992614746,0.0001309134,0.2912621359,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,6.188244104385376,0.0,0.730558598,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1
+qft_indep_tket_5,qft,5,-1,0.8571389314,0.3461538462,-1,0.0249825829,0.502994012,0.0818929672241211,0.2235351219,0.1621621622,1.097163200378418,0.6977721389,0.2380952381,0.5466320514678955,0.0095684003,0.22,0.13639402389526367,0.0046324367,0.1842105263,1.9603219032287598,0.315485493,0.3461538462,4.431833744049072,0.7619202122,0.3461538462,4.58465313911438,0.8571389314,0.3461538462,0.6055927276611328,0.3118028662,0.1428571429,0.557318925857544,0.7318409102,0.125,0.46144580841064453,0.0130335095,0.2045454545,0.5095479488372803,6.558e-05,0.2571428571,0.43320608139038086,0.2873605232,0.3,0.4456520080566406,0.767921725,0.3,0.40137505531311035,0.8153546164,0.3
+wstate_indep_qiskit_6,wstate,6,-1,0.9295389164,0.3,-1,0.1285621854,0.3846153846,0.20799803733825684,0.7854060373,0.3,0.03406381607055664,0.7791675508,0.3,0.0769662857055664,0.2525370366,0.3,0.08663296699523926,0.1559166132,0.3,3.8074729442596436,0.6273287911,0.3,3.8602569103240967,0.8976016244,0.3,3.8843657970428467,0.9295389164,0.3,0.3150289058685303,0.788440806,0.3,0.287506103515625,0.7160607949,0.3,0.28591179847717285,0.2277351578,0.3,0.29906630516052246,0.0838706631,0.3,0.27730417251586914,0.4690013966,0.3,0.2889139652252197,0.8586122835,0.3,0.3233480453491211,0.8186242047,0.3
+qftentangled_indep_qiskit_30,qftentangled,30,-1,0.0,0.6434151786,-1,0.0076789444,0.873940678,4.345346927642822,0.0,0.7752659574,-1,-1,-1,-1,-1,-1,7.0928261280059814,0.0,0.7952538631,-1,-1,-1,-1,-1,-1,93.33109211921692,0.0076789444,0.873940678,18.835965871810913,0.0,0.7463863338,-1,-1,-1,-1,-1,-1,19.960041999816895,0.0,0.8385036496,-1,-1,-1,-1,-1,-1,15.299785852432251,0.0002693482,0.8728448276
+qft_indep_qiskit_16,qft,16,-1,0.3255358131,0.7583333333,-1,0.2445102407,0.7689393939,0.9023940563201904,1.37182e-05,0.5944540728,1.974086046218872,0.0035138081,0.5721925134,-1,-1,-1,5.359977960586548,0.0,0.5559566787,-1,-1,-1,44.948590993881226,0.0686942972,0.7689393939,67.57089400291443,0.2445102407,0.7689393939,11.957988977432251,2.57e-08,0.59972106,5.01204514503479,0.0005630552,0.5593220339,-1,-1,-1,5.459630250930786,0.0,0.6076388889,-1,-1,-1,4.70881199836731,0.0461808101,0.7583333333,3.9282689094543457,0.11608441,0.7583333333
+qnn_indep_qiskit_5,qnn,5,-1,0.7991197836,0.3636363636,-1,1.5985e-06,0.4791666667,0.14524006843566895,0.4701126783,0.1408450704,0.380357027053833,0.4839035882,0.1408450704,4.461021900177002,0.0029985081,0.2409638554,0.712982177734375,1.19e-08,0.2191780822,1.2857601642608643,0.158702507,0.3720930233,4.968497037887573,0.6443975685,0.3720930233,4.881315231323242,0.7991197836,0.3636363636,1.0319159030914307,0.2203782046,0.1818181818,0.9201691150665283,0.4001393239,0.1818181818,0.9124999046325684,8.89878e-05,0.359223301,0.8991560935974121,1.898e-07,0.2025316456,0.7704060077667236,0.0723061857,0.3720930233,0.7838799953460693,0.5715156526,0.3720930233,0.923450231552124,0.650322207,0.3333333333
+wstate_indep_tket_23,wstate,23,-1,0.7325029164,0.4545454545,-1,8.58764e-05,0.5623342175,2.1791799068450928,0.2151287869,0.4545454545,0.37340688705444336,0.2795365755,0.3050847458,-1,-1,-1,10.468235969543457,5.30429e-05,0.4545454545,-1,-1,-1,9.588814735412598,0.6250733829,0.4545454545,9.211106777191162,0.7325029164,0.4545454545,1.3441498279571533,0.2175020051,0.4545454545,2.3143458366394043,0.1514378134,0.4159292035,-1,-1,-1,1.3281259536743164,5.15e-08,0.4545454545,-1,-1,-1,1.2822389602661133,0.5148266516,0.4545454545,1.4430031776428223,0.4216565576,0.4545454545
+su2random_indep_tket_2,su2random,2,-1,0.9723659696,0.0,-1,0.9363309001,0.0,0.043457984924316406,0.9243693585,0.0,0.03344368934631348,0.9390531224,0.0,0.033940792083740234,0.7757092761,0.0,0.03194022178649902,0.3153214239,0.0,0.02295517921447754,0.8374806253,0.0,0.03090381622314453,0.9625879651,0.0,0.027846813201904297,0.9723659696,0.0,0.07805895805358887,0.9155385386,0.0,0.05379295349121094,0.9105576282,0.0,0.052561283111572266,0.5699657914,0.0,0.06497716903686523,0.5574869934,0.0,0.05061817169189453,0.8030938944,0.0,0.06473302841186523,0.9564458494,0.0,0.08079910278320312,0.961723264,0.0
+qftentangled_indep_qiskit_7,qftentangled,7,-1,0.718681808,0.5263157895,-1,0.0131208232,0.5306122449,0.16410589218139648,0.0675418068,0.3684210526,0.14078688621520996,0.3819485133,0.3333333333,1.1241722106933594,4.83655e-05,0.4210526316,0.3111691474914551,0.0,0.2916666667,7.840548753738403,0.0847231631,0.5263157895,6.8741981983184814,0.5560822071,0.5263157895,9.620162963867188,0.718681808,0.5263157895,1.359071969985962,0.336785112,0.325203252,1.2790770530700684,0.2460478139,0.3513513514,1.3053779602050781,0.0001499415,0.4054054054,1.4104578495025635,4e-10,0.2365591398,1.071976900100708,0.0515030486,0.5,1.08331298828125,0.5327616633,0.5,0.993596076965332,0.5972981429,0.5185185185
+qpeexact_indep_tket_2,qpeexact,2,-1,0.9870717764,0.0,-1,0.9731398825,0.0,0.025705814361572266,0.9717261763,0.0,0.015780210494995117,0.9763339593,0.0,0.0198819637298584,0.9031130253,0.0,0.02860093116760254,0.6437459071,0.0,0.014920949935913086,0.9492042272,0.0,0.023182153701782227,0.9884244567,0.0,0.0276491641998291,0.9870717764,0.0,0.05720186233520508,0.9803381684,0.0,0.031430959701538086,0.9541361859,0.0,0.029148101806640625,0.7803463727,0.0,0.044734954833984375,0.7995528091,0.0,0.03158712387084961,0.9174138715,0.0,0.04490303993225098,0.9832969669,0.0,0.049848079681396484,0.9762710499,0.0
+qftentangled_indep_tket_7,qftentangled,7,-1,0.718681808,0.5263157895,-1,2.1201e-06,0.5348837209,0.16204476356506348,0.0675418068,0.3684210526,0.14373087882995605,0.3819485133,0.3333333333,1.621062994003296,4.82058e-05,0.4210526316,0.31071019172668457,0.0,0.2916666667,6.485971212387085,0.0847231631,0.5263157895,6.473883867263794,0.5560822071,0.5263157895,6.51059889793396,0.718681808,0.5263157895,1.3470900058746338,0.336785112,0.325203252,1.2819719314575195,0.2460478139,0.3513513514,1.2969369888305664,0.0001499415,0.4054054054,1.40488600730896,4e-10,0.2365591398,1.066985845565796,0.0515030486,0.5,1.0813839435577393,0.5327616633,0.5,0.9837141036987305,0.5972981429,0.5185185185
+qftentangled_indep_qiskit_10,qftentangled,10,-1,0.5269428777,0.6578947368,-1,0.0001237193,0.6455696203,0.34206509590148926,0.003747606,0.3761061947,0.4216279983520508,0.0872276209,0.4233870968,-1,-1,-1,0.6344308853149414,0.0,0.3849765258,10.639816999435425,0.0075246937,0.6578947368,21.140341997146606,0.3114623416,0.6578947368,10.687345027923584,0.5269428777,0.6578947368,9.475258111953735,0.002139567,0.4082397004,3.3922290802001953,0.0305122855,0.4750957854,-1,-1,-1,9.148578882217407,0.0,0.5657894737,2.09969162940979,0.0023287622,0.6363636364,2.109673023223877,0.2751907968,0.6363636364,1.876749038696289,0.3694794097,0.6481481481
+su2random_indep_tket_30,su2random,30,-1,0.0125993391,0.9103448276,-1,2.4e-09,0.9103448276,10.151202917098999,0.0,0.802241594,-1,-1,-1,-1,-1,-1,13.421740055084229,0.0,0.7673469388,-1,-1,-1,-1,-1,-1,100.78143620491028,0.0125993391,0.9103448276,35.66151213645935,0.0,0.7258064516,-1,-1,-1,-1,-1,-1,37.03677201271057,0.0,0.7909774436,-1,-1,-1,-1,-1,-1,37.757853984832764,1.7e-09,0.9103448276
+qnn_indep_tket_2,qnn,2,-1,0.9713936037,0.0,-1,0.9363309001,0.0,0.04443502426147461,0.9243693585,0.0,0.036241769790649414,0.9453543412,0.0,0.03644061088562012,0.7757092761,0.0,0.03238201141357422,0.3153214239,0.0,0.025491952896118164,0.8374806253,0.0,0.032961130142211914,0.9625879651,0.0,0.033715009689331055,0.9675080332,0.0,0.08796191215515137,0.9155385386,0.0,0.060402870178222656,0.9105576282,0.0,0.06330394744873047,0.5699657914,0.0,0.07739806175231934,0.5574869934,0.0,0.06155085563659668,0.8030938944,0.0,0.07360601425170898,0.9564458494,0.0,0.09529614448547363,0.961723264,0.0
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+vqe_indep_tket_12,vqe,12,-1,0.830141394,0.4090909091,-1,0.0244149104,0.5864661654,0.18454384803771973,0.4517625497,0.4090909091,0.08046483993530273,0.5810471843,0.4090909091,-1,-1,-1,0.91255784034729,0.035223507,0.4090909091,-1,-1,-1,5.865538835525513,0.7833740742,0.4090909091,5.832872152328491,0.830141394,0.4090909091,0.6637308597564697,0.6271100233,0.4090909091,0.6306450366973877,0.5498535982,0.4090909091,-1,-1,-1,0.6804900169372559,0.0051888767,0.4090909091,-1,-1,-1,0.6286687850952148,0.7076785756,0.4090909091,0.6991219520568848,0.6273194792,0.4090909091
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+twolocalrandom_indep_tket_4,twolocalrandom,4,-1,0.9056488079,0.2777777778,-1,0.4394671195,0.2753623188,0.08150100708007812,0.4269611715,0.0,0.06446623802185059,0.6568596526,0.0,0.5045831203460693,0.0063994505,0.2083333333,0.5097799301147461,0.0145949606,0.0,0.17172503471374512,0.4627087621,0.2777777778,25.322060108184814,0.8319607743,0.2777777778,19.059033155441284,0.9056488079,0.2777777778,0.5735619068145752,0.2534812171,0.0,0.44304895401000977,0.6261643669,0.0,0.5355010032653809,0.0040020039,0.2222222222,0.8197929859161377,1.58732e-05,0.1944444444,0.4025309085845947,0.2812434682,0.2777777778,0.41374897956848145,0.7710630055,0.2777777778,0.4812462329864502,0.7202058149,0.2777777778
+dj_indep_tket_15,dj,15,-1,0.8966437539,0.0,-1,0.0200204046,0.4587155963,0.09754300117492676,0.3820567194,0.0555555556,0.1524028778076172,0.4331550386,0.0,-1,-1,-1,0.15905308723449707,0.0003359884,0.0967741935,-1,-1,-1,6.847847938537598,0.8619138286,0.0,6.774635076522827,0.8966437539,0.0,0.9515583515167236,0.3153391162,0.085106383,0.9613239765167236,0.3948771776,0.2461538462,-1,-1,-1,1.0172529220581055,0.0008674667,0.5076923077,-1,-1,-1,0.5885040760040283,0.8201976733,0.0,0.6519529819488525,0.7849280723,0.0
+qpeexact_indep_tket_30,qpeexact,30,-1,0.0117981931,0.8083623693,-1,0.0100013136,0.8142857143,4.967839002609253,0.0,0.7220245156,-1,-1,-1,-1,-1,-1,7.630677938461304,0.0,0.6732477789,-1,-1,-1,-1,-1,-1,89.63144302368164,0.0100013136,0.8142857143,21.171550035476685,0.0,0.6945796819,-1,-1,-1,-1,-1,-1,18.428704977035522,0.0,0.7408730159,-1,-1,-1,-1,-1,-1,13.718029737472534,0.0006155453,0.8087557604
+pricingcall_indep_qiskit_5,pricingcall,5,-1,0.7669888696,0.06,-1,0.0121859364,0.3659574468,0.19251775741577148,0.1707973501,0.0857142857,0.17293810844421387,0.4252517556,0.0857142857,0.7001690864562988,0.0015297327,0.0857142857,0.39459776878356934,1.30254e-05,0.0882352941,1.200376033782959,0.1218999813,0.0652173913,4.9125590324401855,0.6168247408,0.0652173913,23.11106014251709,0.7669888696,0.06,0.9673259258270264,0.3223789208,0.0365853659,0.8543450832366943,0.3852910839,0.0365853659,0.9095942974090576,0.000406219,0.0759493671,1.0678589344024658,5.063e-07,0.1194029851,0.855086088180542,0.0493610119,0.0652173913,0.8644082546234131,0.5332315754,0.0652173913,1.0384900569915771,0.5313036947,0.0571428571
+wstate_indep_qiskit_16,wstate,16,-1,0.8079970983,0.4333333333,-1,0.0019238154,0.5662650602,0.42450499534606934,0.3004558039,0.4333333333,0.08444404602050781,0.4361729232,0.4333333333,-1,-1,-1,6.010483026504517,0.0016792383,0.4333333333,-1,-1,-1,7.516310930252075,0.7255058355,0.4333333333,7.517931938171387,0.8079970983,0.4333333333,1.0714399814605713,0.289831997,0.4333333333,0.9278881549835205,0.4835147165,0.4333333333,-1,-1,-1,1.0945611000061035,0.0011202584,0.4333333333,-1,-1,-1,1.0063691139221191,0.6355194072,0.4333333333,1.0097150802612305,0.5541136674,0.4333333333
+graphstate_indep_tket_18,graphstate,18,-1,0.9138405512,0.6666666667,-1,0.0,0.7516339869,0.12159609794616699,0.2525978855,0.6428571429,0.08528614044189453,0.382953095,0.7,-1,-1,-1,6.11296010017395,0.016664061,0.6666666667,-1,-1,-1,8.091577053070068,0.8319607743,0.6666666667,28.642733097076416,0.9138405512,0.6666666667,1.1129672527313232,0.2216712314,0.7395833333,1.2065389156341553,0.4037352789,0.6458333333,-1,-1,-1,0.6493470668792725,0.0118701001,0.6666666667,-1,-1,-1,0.6622021198272705,0.7497684968,0.6666666667,0.6991682052612305,0.6741846287,0.6666666667
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+wstate_indep_qiskit_20,wstate,20,-1,0.7639530883,0.4473684211,-1,0.0,0.3709677419,1.05708909034729,0.2395976831,0.4473684211,0.09700512886047363,0.3928752756,0.4473684211,-1,-1,-1,9.06552791595459,0.0016471772,0.4473684211,-1,-1,-1,8.297262191772461,0.6662908082,0.4473684211,8.435129880905151,0.7639530883,0.4473684211,1.1542267799377441,0.3014815249,0.4473684211,1.1480629444122314,0.3645924602,0.4473684211,-1,-1,-1,1.1423900127410889,1.78346e-05,0.4473684211,-1,-1,-1,1.1222069263458252,0.5634579429,0.4473684211,1.2140469551086426,0.4740286322,0.4473684211
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+graphstate_indep_tket_80,graphstate,80,-1,0.0,0.8628605062,-1,0.0,0.8673279603,30.827154874801636,0.0457268576,0.8875,-1,-1,-1,-1,-1,-1,2.2829768657684326,3.71e-08,0.8875,-1,-1,-1,-1,-1,-1,-1,-1,-1,3.3427770137786865,0.0320841022,0.8875,-1,-1,-1,-1,-1,-1,3.638021945953369,0.0,0.8604651163,-1,-1,-1,-1,-1,-1,-1,-1,-1
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+qpeexact_indep_qiskit_3,qpeexact,3,-1,0.9569127416,0.0,-1,0.8742967602,0.0,0.04003286361694336,0.8881686899,0.0,2.4339449405670166,0.931840047,0.0,0.05076098442077637,0.7507367928,0.0,0.04233694076538086,0.5074385964,0.0,0.037214040756225586,0.8054240086,0.0,0.18056511878967285,0.953350663,0.0,0.36031103134155273,0.9569127416,0.0,0.13520097732543945,0.9013026635,0.0,0.10695195198059082,0.9305445367,0.0,0.11046195030212402,0.5560751573,0.0,0.13229870796203613,0.5243408865,0.0,0.1100318431854248,0.7563310054,0.0,0.12234377861022949,0.9442404734,0.0,0.13333988189697266,0.9445557658,0.0
+su2random_indep_qiskit_2,su2random,2,-1,0.9723659696,0.0,-1,0.9379018633,0.0,0.04306507110595703,0.9243693585,0.0,0.03233814239501953,0.9390531224,0.0,0.034564971923828125,0.7757092761,0.0,0.03190898895263672,0.3153214239,0.0,0.028091907501220703,0.8374806253,0.0,0.03885006904602051,0.9625879651,0.0,0.03324699401855469,0.9723659696,0.0,0.07974410057067871,0.9155385386,0.0,0.05133533477783203,0.9105576282,0.0,0.05408215522766113,0.5699657914,0.0,0.06705212593078613,0.5574869934,0.0,0.05461597442626953,0.8030938944,0.0,0.08210206031799316,0.9564458494,0.0,0.16533422470092773,0.961723264,0.0
+qpeexact_indep_tket_10,qpeexact,10,-1,0.595976848,0.5408163265,-1,1.16976e-05,0.6103379722,0.2886037826538086,0.0302726655,0.3317757009,0.2740039825439453,0.1280189187,0.3248730964,-1,-1,-1,0.5783522129058838,0.0,0.3114754098,10.049322128295898,0.015804582,0.5463917526,10.037600040435791,0.371372678,0.5463917526,9.997150182723999,0.595976848,0.5408163265,2.1171042919158936,0.0256851879,0.5240384615,1.7748889923095703,0.0597519533,0.3613445378,-1,-1,-1,2.020125150680542,0.0,0.3730569948,1.6182591915130615,0.0059272879,0.5176470588,1.6455259323120117,0.3341779937,0.5176470588,1.823580265045166,0.4567947535,0.511627907
+graphstate_indep_qiskit_20,graphstate,20,-1,0.9047377161,0.65,-1,0.0,0.7267080745,0.1407618522644043,0.3865269666,0.65,0.05391073226928711,0.4635629163,0.65,-1,-1,-1,1.9092001914978027,0.0071918091,0.65,-1,-1,-1,8.391825199127197,0.8160698654,0.65,8.365910768508911,0.9047377161,0.65,0.7271480560302734,0.4567795897,0.65,0.6747770309448242,0.44969507,0.65,-1,-1,-1,0.7070200443267822,0.000523316,0.65,-1,-1,-1,0.6687057018280029,0.7240612906,0.65,0.8383381366729736,0.6406430709,0.65
+dj_indep_qiskit_9,dj,9,-1,0.9379523925,0.0,-1,0.1771979602,0.4054054054,0.06044292449951172,0.5368024147,0.0555555556,0.056344032287597656,0.7468002626,0.0,-1,-1,-1,0.12185311317443848,0.0239557245,0.0,4.676688194274902,0.6962671291,0.0,4.6981201171875,0.9187485456,0.0,4.733787775039673,0.9379523925,0.0,0.5957992076873779,0.4457847465,0.0,0.5195231437683105,0.5634577808,0.2692307692,-1,-1,-1,0.5594358444213867,0.0244218654,0.0588235294,0.27508091926574707,0.5750423952,0.0,0.4059789180755615,0.8923037718,0.0,0.4336388111114502,0.8692764232,0.0
+portfoliovqe_indep_tket_9,portfoliovqe,9,-1,0.689691771,0.6944444444,-1,0.0018105829,0.6944444444,0.693479061126709,0.0001578615,0.4056338028,0.5895030498504639,0.0199096512,0.527607362,-1,-1,-1,0.848128080368042,0.0,0.5172413793,9.26706576347351,0.0162736774,0.6944444444,9.332918167114258,0.3582212491,0.6944444444,9.211841583251953,0.689691771,0.6944444444,3.2370009422302246,0.0002430995,0.4909560724,4.7694010734558105,0.0019318667,0.5422535211,-1,-1,-1,3.097572088241577,0.0,0.5039370079,3.04590106010437,0.000867027,0.6944444444,2.7727229595184326,0.2289402989,0.6944444444,3.041933059692383,0.1711512767,0.6944444444
+portfoliovqe_indep_qiskit_11,portfoliovqe,11,-1,0.5577814736,0.7515151515,-1,6.70199e-05,0.7515151515,0.8889310359954834,0.0007075792,0.5992292871,0.9323577880859375,0.0024247216,0.6308016878,-1,-1,-1,1.4559829235076904,0.0,0.5841836735,0.31182169914245605,0.0018490429,0.7515151515,30.694917917251587,0.2083294313,0.7515151515,82.90333318710327,0.5577814736,0.7515151515,17.43560290336609,8.294e-07,0.6319758673,5.571901798248291,0.0003683166,0.5622119816,-1,-1,-1,17.490500926971436,0.0,0.6156462585,4.181262969970703,2.31901e-05,0.7515151515,4.196130990982056,0.1067603834,0.7515151515,4.499175310134888,0.0704520665,0.7515151515
+graphstate_indep_tket_3,graphstate,3,-1,0.9850956568,0.0,-1,0.1389093323,0.4736842105,1.858083724975586,0.8762002776,0.0,0.19192099571228027,0.8918969536,0.0,2.468303680419922,0.4413663922,0.0,0.37453699111938477,0.3672082753,0.0,3.093306064605713,0.8779287448,0.0,0.7781310081481934,0.9695450053,0.0,1.892637014389038,0.9850956568,0.0,0.2454671859741211,0.732348255,0.0,0.09598994255065918,0.8493202509,0.0,0.09344315528869629,0.4115176314,0.0,0.17901206016540527,0.4210150639,0.0,0.09026098251342773,0.7884971531,0.0,0.10381102561950684,0.9537710125,0.0,0.15274429321289062,0.9379664995,0.0
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+dj_indep_tket_19,dj,19,-1,0.8709913874,0.0,-1,0.0,0.468,4.7430689334869385,0.195833192,0.0576923077,4.06357216835022,0.3932900698,0.0,-1,-1,-1,4.729588985443115,8.6308e-05,0.0377358491,-1,-1,-1,9.45198678970337,0.827313888,0.0,15.296777963638306,0.8709913874,0.0,1.3696749210357666,0.0822722916,0.2688172043,0.585921049118042,0.1489559441,0.5,-1,-1,-1,1.103322982788086,6.35e-08,0.4285714286,-1,-1,-1,0.8763940334320068,0.7753939398,0.0,0.6562039852142334,0.7332935001,0.0
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+qpeinexact_indep_tket_7,qpeinexact,7,-1,0.7601112306,0.3529411765,-1,0.0115882082,0.4716981132,6.089564800262451,0.2309955475,0.2134831461,1.8347132205963135,0.2790539813,0.2087912088,25.025359869003296,8.20243e-05,0.3211009174,0.2665860652923584,1.07e-08,0.1578947368,12.498149156570435,0.1110799653,0.3529411765,6.488672971725464,0.5925132446,0.3529411765,6.434324026107788,0.7601112306,0.3529411765,1.0855591297149658,0.2842956932,0.2555555556,1.032466173171997,0.1839835032,0.2688172043,1.037686824798584,0.0005470988,0.21875,1.1477508544921875,6.11e-08,0.2533333333,0.8206591606140137,0.0765758532,0.2857142857,0.8354907035827637,0.5786715776,0.2857142857,0.7539021968841553,0.6782378117,0.2857142857
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+twolocalrandom_indep_tket_8,twolocalrandom,8,-1,0.7065040889,0.6547619048,-1,0.0175339314,0.6547619048,12.471486330032349,0.0029355974,0.3555555556,0.7568368911743164,0.1069855221,0.3502109705,9.414208173751831,3e-10,0.4907749077,2.2040457725524902,0.0,0.3979057592,9.159287214279175,0.0363287977,0.6547619048,7.917426347732544,0.442380602,0.6547619048,25.099799871444702,0.7065040889,0.6547619048,2.5736701488494873,0.000970306,0.4833333333,2.292588949203491,0.0281832132,0.4833333333,2.2276387214660645,0.0,0.6374269006,2.1622800827026367,0.0,0.619047619,1.9034781455993652,0.0037955182,0.6547619048,1.9145269393920898,0.3133439985,0.6547619048,2.410799026489258,0.247845394,0.6547619048
+qnn_indep_tket_30,qnn,30,-1,0.0005991317,0.8852459016,-1,0.0005991317,0.8852459016,6.167135000228882,0.0,0.8376511226,-1,-1,-1,-1,-1,-1,10.542107105255127,0.0,0.7700518451,-1,-1,-1,-1,-1,-1,90.8837308883667,0.0005991317,0.8852459016,29.481762886047363,0.0,0.7276844688,-1,-1,-1,-1,-1,-1,31.12676692008972,0.0,0.7660377358,-1,-1,-1,-1,-1,-1,33.005688190460205,3.077e-07,0.8709677419
+pricingcall_indep_qiskit_9,pricingcall,9,-1,0.4705674736,0.2260273973,-1,7.80029e-05,0.3300395257,2.7055671215057373,0.0193308976,0.1660377358,1.6608140468597412,0.0204243615,0.1589147287,-1,-1,-1,0.8557941913604736,0.0,0.2478632479,7.805736780166626,0.0018086468,0.1338028169,7.841779947280884,0.2259861593,0.1338028169,19.783050060272217,0.4682165203,0.1621621622,3.0207722187042236,0.0137381172,0.2572347267,2.960566997528076,0.0145242236,0.238410596,-1,-1,-1,3.182292938232422,0.0,0.2012987013,2.6878840923309326,9.70676e-05,0.2142857143,2.701011896133423,0.145658869,0.2142857143,3.2092857360839844,0.1351539779,0.203539823
+vqe_indep_tket_15,vqe,15,-1,0.7991582448,0.4285714286,-1,0.0222151969,0.49375,0.3578062057495117,0.3103314181,0.4285714286,0.0927281379699707,0.4433439653,0.4285714286,-1,-1,-1,3.869901657104492,0.0025611284,0.4285714286,-1,-1,-1,7.118758916854858,0.7331517292,0.4285714286,7.318278074264526,0.7991582448,0.4285714286,0.8782908916473389,0.3888051582,0.4285714286,1.0436210632324219,0.5091994018,0.4285714286,-1,-1,-1,0.9070138931274414,0.0016726913,0.4285714286,-1,-1,-1,0.9450299739837646,0.6453076315,0.4285714286,1.0530359745025635,0.5552268996,0.4285714286
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+graphstate_indep_tket_14,graphstate,14,-1,0.9323219019,0.7142857143,-1,0.0014315112,0.7746478873,0.07340312004089355,0.4077355836,0.7,0.057836055755615234,0.6019025512,0.7,-1,-1,-1,0.12175774574279785,0.0100021889,0.5769230769,-1,-1,-1,6.351963043212891,0.8660617917,0.7142857143,33.91340398788452,0.9323219019,0.7142857143,0.8554160594940186,0.1567681961,0.4153846154,0.736419677734375,0.5170638058,0.6285714286,-1,-1,-1,0.8307771682739258,0.0008773664,0.6571428571,-1,-1,-1,0.45945167541503906,0.800743423,0.7142857143,0.5066468715667725,0.739195229,0.7142857143
+groundstate_medium_indep_tket_12,groundstate,12,-1,0.6183892171,0.75,-1,0.2775836184,0.75,0.7910759449005127,0.0026669642,0.6252983294,0.7085800170898438,0.0091756716,0.4710920771,-1,-1,-1,1.2319087982177734,0.0,0.4490358127,-1,-1,-1,36.654964208602905,0.2878759037,0.75,60.31043982505798,0.6183892171,0.75,13.809936285018921,1.15459e-05,0.5649452269,3.8390519618988037,0.0011195289,0.5254237288,-1,-1,-1,15.180864095687866,0.0,0.7264957265,-1,-1,-1,3.2430367469787598,0.1674728332,0.75,3.5029349327087402,0.121912798,0.75
+qaoa_indep_tket_8,qaoa,8,-1,0.9156764862,0.6875,-1,0.359597462,0.6875,0.12596607208251953,0.3266612534,0.66,0.10756516456604004,0.4770712887,0.66,0.1669609546661377,0.0414409216,0.6875,2.427060842514038,0.0096156869,0.6875,22.721914768218994,0.5135566962,0.6875,8.097859144210815,0.851561239,0.6875,5.040174245834351,0.9156764862,0.6875,0.8676848411560059,0.2564390556,0.7076923077,0.837752103805542,0.4474267956,0.75,0.645103931427002,0.0372172437,0.6875,0.6810059547424316,0.0012061682,0.6875,0.6415131092071533,0.1384273669,0.6875,0.6521799564361572,0.657186516,0.6875,0.5350620746612549,0.7369754264,0.6875
+qnn_indep_tket_5,qnn,5,-1,0.7991197836,0.3636363636,-1,0.0014746384,0.3333333333,0.14505696296691895,0.4701126783,0.1408450704,0.12832093238830566,0.4839035882,0.1408450704,32.3403639793396,0.002985917,0.2409638554,2.411757707595825,1.19e-08,0.2191780822,1.0653057098388672,0.158702507,0.3720930233,20.18013024330139,0.6443975685,0.3720930233,4.553439140319824,0.7991197836,0.3636363636,0.9980778694152832,0.2203970996,0.1818181818,0.9413719177246094,0.4002169239,0.1818181818,0.8421399593353271,9.10689e-05,0.359223301,0.8895628452301025,1.887e-07,0.2025316456,0.7690789699554443,0.0724959057,0.3720930233,0.7858428955078125,0.5717443504,0.3720930233,0.9110729694366455,0.6444926641,0.3333333333
+su2random_indep_tket_5,su2random,5,-1,0.8571286183,0.4333333333,-1,0.0053361286,0.507751938,5.9334800243377686,0.5175108606,0.1333333333,0.20363211631774902,0.2761491803,0.1333333333,1.1587419509887695,0.001911216,0.2888888889,1.7450649738311768,3.33e-08,0.2089552239,1.0461499691009521,0.2727286202,0.4333333333,4.499313831329346,0.7342661142,0.4333333333,4.54187798500061,0.8571286183,0.4333333333,0.9547860622406006,0.1229713869,0.2528735632,0.8968520164489746,0.4768625354,0.2666666667,0.8006830215454102,4.2102e-05,0.4035087719,0.8501451015472412,5.7e-09,0.2345679012,0.7282280921936035,0.1216797078,0.4333333333,0.7414720058441162,0.6491390002,0.4333333333,0.9154889583587646,0.5848742458,0.4333333333
+qpeexact_indep_tket_5,qpeexact,5,-1,0.8657576525,0.1923076923,-1,1.2e-09,0.4173228346,0.5803890228271484,0.3181452398,0.0555555556,0.2838759422302246,0.7440622676,0.0588235294,0.3789949417114258,0.0500766873,0.0555555556,0.1533660888671875,5.57321e-05,0.1351351351,1.0379669666290283,0.3323684029,0.2,4.495522975921631,0.7708431403,0.2,4.4877729415893555,0.8657576525,0.1923076923,0.6026220321655273,0.5676726039,0.0,0.5450210571289062,0.7275143492,0.1176470588,0.44306468963623047,0.0170217587,0.1162790698,0.5144309997558594,0.0001181448,0.064516129,0.503532886505127,0.3035326894,0.1052631579,0.43877482414245605,0.7772258279,0.1052631579,0.40154385566711426,0.8276833351,0.1
+vqe_indep_tket_7,vqe,7,-1,0.9029508739,0.3333333333,-1,0.3979128231,0.4166666667,0.792978048324585,0.6035129981,0.3333333333,0.04700303077697754,0.7555248754,0.3333333333,0.33316469192504883,0.185790886,0.3333333333,0.138197660446167,0.049861118,0.3333333333,4.3632800579071045,0.5563990508,0.3333333333,4.427907228469849,0.874838888,0.3333333333,4.442906856536865,0.9029508739,0.3333333333,0.3974580764770508,0.7618750868,0.3333333333,0.36743593215942383,0.6713590215,0.3333333333,0.4019432067871094,0.1992896527,0.3333333333,0.39441514015197754,0.0366932461,0.3333333333,0.36083030700683594,0.389596101,0.3333333333,2.000152111053467,0.82850996,0.3333333333,0.4104180335998535,0.774789669,0.3333333333
+qftentangled_indep_qiskit_40,qftentangled,40,-1,0.0,0.388374676,-1,0.0,0.8202027183,9.661148309707642,0.0,0.7400218699,-1,-1,-1,-1,-1,-1,12.90355396270752,0.0,0.7766593268,-1,-1,-1,-1,-1,-1,-1,-1,-1,33.03002691268921,0.0,0.7765531062,-1,-1,-1,-1,-1,-1,35.03521800041199,0.0,0.8482014388,-1,-1,-1,-1,-1,-1,-1,-1,-1
+wstate_indep_tket_24,wstate,24,-1,0.722309922,0.4565217391,-1,5.60329e-05,0.6112469438,2.6985929012298584,0.1976409407,0.4565217391,0.1754150390625,0.2261227433,0.3888888889,-1,-1,-1,10.422148704528809,6.37239e-05,0.4565217391,-1,-1,-1,9.876495122909546,0.6119088494,0.4565217391,9.778534173965454,0.722309922,0.4565217391,1.4015450477600098,0.2117995845,0.4565217391,2.0839531421661377,0.1072177005,0.4461538462,-1,-1,-1,1.3848061561584473,4.374e-07,0.4565217391,-1,-1,-1,1.3420207500457764,0.4995675188,0.4565217391,1.9404170513153076,0.4055181407,0.4565217391
+tsp_indep_qiskit_9,tsp,9,-1,0.795141735,0.6,-1,0.3024772341,0.6,0.19400596618652344,0.2970586081,0.6,0.14839386940002441,0.4816365949,0.6,-1,-1,-1,0.32303786277770996,0.0025353843,0.6,4.80271315574646,0.1688635037,0.6,4.794290065765381,0.657761354,0.6,11.80482006072998,0.795141735,0.6,1.213672161102295,0.529253429,0.6,1.1827776432037354,0.4522166463,0.6,-1,-1,-1,1.1653640270233154,0.0001994647,0.6,1.1416850090026855,0.0551566566,0.6,1.1568272113800049,0.5544673996,0.6,1.2620317935943604,0.4508959903,0.6
+wstate_indep_tket_25,wstate,25,-1,0.7122587661,0.4583333333,-1,3.91688e-05,0.618705036,3.509429931640625,0.1859356382,0.4583333333,0.17251992225646973,0.2955115261,0.3,-1,-1,-1,10.59879183769226,2.06923e-05,0.4583333333,-1,-1,-1,0.1302177906036377,0.5990215713,0.4583333333,10.035637855529785,0.7122587661,0.4583333333,1.5965640544891357,0.2039155,0.4583333333,2.4461262226104736,0.148530344,0.5213675214,-1,-1,-1,1.4634931087493896,3.606e-07,0.4583333333,-1,-1,-1,1.4147181510925293,0.484760657,0.4583333333,1.5423166751861572,0.389997403,0.4583333333
+dj_indep_qiskit_60,dj,60,-1,0.0002370945,0.3253012048,-1,0.0,0.8776206728,0.7136662006378174,0.0031936312,0.1976744186,-1,-1,-1,-1,-1,-1,0.8166050910949707,0.0,0.2105263158,-1,-1,-1,-1,-1,-1,-1,-1,-1,4.299183130264282,3e-10,0.7806505421,-1,-1,-1,-1,-1,-1,3.895965099334717,0.0,0.7405764967,-1,-1,-1,-1,-1,-1,-1,-1,-1
+su2random_indep_tket_4,su2random,4,-1,0.9011296113,0.2777777778,-1,0.4901223002,0.2753623188,0.08586788177490234,0.4269611715,0.0,0.6300442218780518,0.6568596526,0.0,0.5025949478149414,0.0063598623,0.2083333333,0.1662449836730957,0.0142044464,0.0,0.16474127769470215,0.4425488854,0.2777777778,3.6998021602630615,0.8263215083,0.2777777778,3.7214159965515137,0.9011296113,0.2777777778,0.5244600772857666,0.2526484057,0.0,0.41841673851013184,0.6241947626,0.0,0.43438076972961426,0.003859583,0.2222222222,0.5470330715179443,1.57299e-05,0.1944444444,0.40749597549438477,0.273252497,0.2777777778,0.4209253787994385,0.7676771055,0.2777777778,0.48886895179748535,0.7137498296,0.2777777778
+qpeexact_indep_tket_4,qpeexact,4,-1,0.9351329095,0.0,-1,0.2461816566,0.4776119403,0.047888994216918945,0.6767894051,0.0,1.983262062072754,0.687453147,0.0,0.22664713859558105,0.1365864336,0.0,0.06600308418273926,0.1093932278,0.0,0.15788912773132324,0.6439846233,0.0,3.3940680027008057,0.9011999424,0.0,3.5674610137939453,0.9351329095,0.0,0.3095729351043701,0.8003728091,0.1,0.23065781593322754,0.8238057095,0.1,0.16182398796081543,0.25630854,0.1,0.2953670024871826,0.1552110068,0.1,0.16679883003234863,0.626808045,0.1428571429,0.17656588554382324,0.9074611249,0.1428571429,0.18006491661071777,0.9184621713,0.25
+vqe_indep_tket_6,vqe,6,-1,0.9175271494,0.3,-1,0.6560192414,0.3548387097,0.06421089172363281,0.7640605952,0.3,0.04145097732543945,0.7745679029,0.3,0.07651185989379883,0.2484745551,0.3,0.3101630210876465,0.1530358056,0.3,3.779332160949707,0.6127071802,0.3,3.7803258895874023,0.8943754239,0.3,3.8407821655273438,0.9175271494,0.3,0.3058309555053711,0.7860912609,0.3,0.2742280960083008,0.7132739307,0.3,0.3036949634552002,0.2326591391,0.3,0.297544002532959,0.0823612955,0.3,0.26947021484375,0.4521075368,0.3,0.2796900272369385,0.8538165361,0.3,0.31900596618652344,0.8072376635,0.3
+qftentangled_indep_qiskit_16,qftentangled,16,-1,0.2588665774,0.7647058824,-1,0.010070567,0.7741935484,1.0227489471435547,1.3602e-06,0.581512605,1.2275269031524658,0.0074506452,0.6300813008,-1,-1,-1,1.9788100719451904,0.0,0.55,-1,-1,-1,49.58464503288269,0.0586169242,0.7741935484,45.00372505187988,0.2207787432,0.7741935484,6.7507829666137695,6.806e-07,0.763546798,5.459743022918701,0.0001200271,0.5077658303,-1,-1,-1,6.582633972167969,0.0,0.5660377358,-1,-1,-1,5.258420944213867,0.037423966,0.7647058824,4.535730838775635,0.0886891792,0.7703703704
+random_indep_qiskit_30,random,30,-1,0.0,0.7114446529,-1,0.0,0.7766023647,11.71191668510437,0.0,0.7217232852,-1,-1,-1,-1,-1,-1,21.186822175979614,0.0,0.6903625954,-1,-1,-1,-1,-1,-1,85.26051878929138,0.0001486019,0.7766023647,39.24835467338562,0.0,0.6951926009,-1,-1,-1,-1,-1,-1,42.61959481239319,0.0,0.6952094423,-1,-1,-1,-1,-1,-1,39.12635326385498,9e-10,0.7782571182
+qaoa_indep_tket_9,qaoa,9,-1,0.9056488079,0.6666666667,-1,2.939e-07,0.6666666667,0.13691091537475586,0.3136755015,0.6111111111,0.11432099342346191,0.4759053891,0.6666666667,-1,-1,-1,0.5794486999511719,5.23799e-05,0.5641025641,5.000687837600708,0.4725110246,0.6666666667,5.0261900424957275,0.8346278473,0.6666666667,5.049088954925537,0.9056488079,0.6666666667,1.006105899810791,0.2703468583,0.6140350877,0.9664351940155029,0.4442453656,0.6140350877,-1,-1,-1,0.9255931377410889,1.40893e-05,0.5740740741,0.6452627182006836,0.1080413982,0.6666666667,0.6552479267120361,0.6235284679,0.6666666667,0.6131808757781982,0.7003099452,0.6666666667
+qft_indep_qiskit_30,qft,30,-1,0.0,0.6518289996,-1,2.685e-07,0.8721311475,3.5751399993896484,0.0,0.7536873156,-1,-1,-1,-1,-1,-1,6.082282066345215,0.0,0.7362804878,-1,-1,-1,-1,-1,-1,93.19664216041565,0.0093714599,0.8721311475,17.12723183631897,0.0,0.7610136452,-1,-1,-1,-1,-1,-1,18.08391809463501,0.0,0.7008230453,-1,-1,-1,-1,-1,-1,13.819219827651978,0.0004554815,0.8689655172
+qnn_indep_tket_4,qnn,4,-1,0.8657550483,0.2222222222,-1,0.0144353533,0.5329949239,0.09215521812438965,0.3827816582,0.0,0.23833703994750977,0.7197849462,0.0,0.7670648097991943,0.0130504991,0.0892857143,0.16411709785461426,0.0088904154,0.0,0.18062901496887207,0.3188096841,0.2307692308,3.8438260555267334,0.7631405046,0.2307692308,3.7238078117370605,0.8657550483,0.2222222222,0.5662779808044434,0.1830758743,0.0,0.46114397048950195,0.5919630395,0.0,0.49523305892944336,0.0044270385,0.1935483871,0.6107578277587891,6.17182e-05,0.2105263158,0.4364900588989258,0.199968947,0.2307692308,0.44672203063964844,0.7106840224,0.2307692308,0.5470800399780273,0.7624786959,0.2
+vqe_indep_tket_14,vqe,14,-1,0.8080068203,0.4230769231,-1,0.0151297402,0.595890411,0.2794339656829834,0.3355444881,0.4230769231,0.0907440185546875,0.4708861485,0.4230769231,-1,-1,-1,2.3339250087738037,0.0033538204,0.4230769231,-1,-1,-1,6.658374071121216,0.7489246674,0.4230769231,6.671196937561035,0.805585223,0.4230769231,0.846923828125,0.5459642625,0.4230769231,0.8128619194030762,0.5208006979,0.4230769231,-1,-1,-1,0.8652150630950928,0.0019533169,0.4230769231,-1,-1,-1,0.8164563179016113,0.6650183502,0.4230769231,0.8895840644836426,0.5773232802,0.4230769231
+graphstate_indep_tket_15,graphstate,15,-1,0.9276668158,0.8,-1,0.8149309249,0.8,0.08398199081420898,0.4435413564,0.75,0.06525278091430664,0.5135188423,0.7272727273,-1,-1,-1,1.006392002105713,0.0227348795,0.7,-1,-1,-1,6.881594181060791,0.8574076853,0.8,7.192640066146851,0.9276668158,0.8,0.8086020946502686,0.2058198467,0.5964912281,0.9041030406951904,0.4277667435,0.5333333333,-1,-1,-1,0.8829669952392578,1.4437e-06,0.6470588235,-1,-1,-1,0.506458044052124,0.7876836526,0.8,0.5579230785369873,0.7223772796,0.8
+random_indep_tket_4,random,4,-1,0.858860951,0.25,-1,0.0258753061,0.4381443299,0.12285494804382324,0.6637928773,0.1714285714,0.10837912559509277,0.5506712056,0.1714285714,0.4969038963317871,0.0203991332,0.1714285714,0.17940688133239746,0.0025927268,0.21875,0.2172698974609375,0.3252932634,0.2608695652,4.132512092590332,0.7739963712,0.2608695652,4.09049916267395,0.858860951,0.25,0.6013321876525879,0.4461918497,0.0,0.5041439533233643,0.6137227348,0.0,0.5277669429779053,0.0220919695,0.1388888889,0.6711840629577637,0.0002665348,0.1818181818,0.5110888481140137,0.2067837859,0.2083333333,0.5244400501251221,0.719611786,0.2083333333,0.6102859973907471,0.730362526,0.1578947368
+qpeinexact_indep_tket_6,qpeinexact,6,-1,0.8226652919,0.25,-1,0.0114770523,0.4444444444,0.11299300193786621,0.3659684354,0.1111111111,0.09081697463989258,0.4806388006,0.1379310345,0.6784090995788574,0.0020965759,0.2463768116,0.2045607566833496,0.0004568024,0.1296296296,5.740894079208374,0.2107595587,0.25,5.772900104522705,0.6904992093,0.25,9.052907705307007,0.8226652919,0.25,0.838331937789917,0.4738641331,0.15,0.6373529434204102,0.4887323599,0.1754385965,0.7138490676879883,0.0036827035,0.2361111111,0.839709997177124,2.017e-07,0.137254902,0.6104679107666016,0.1589633447,0.2,0.6225929260253906,0.6761771054,0.2,0.5630660057067871,0.7571573304,0.2
+dj_indep_qiskit_21,dj,21,-1,0.8580124137,0.0,-1,0.0,0.4912280702,0.1367330551147461,0.2522181483,0.0357142857,0.1286780834197998,0.3566388907,0.0588235294,-1,-1,-1,0.3587062358856201,7.95271e-05,0.0217391304,-1,-1,-1,12.378050088882446,0.8111871324,0.0,9.836472749710083,0.8580124137,0.0,1.4433190822601318,0.1306021862,0.3663366337,1.179569959640503,0.0961582825,0.4790419162,-1,-1,-1,1.4075441360473633,1e-10,0.2788461538,-1,-1,-1,0.6402380466461182,0.7539183903,0.0,0.7200336456298828,0.7087642806,0.0
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+realamprandom_indep_qiskit_4,realamprandom,4,-1,0.9056488079,0.2777777778,-1,0.0236255828,0.5128205128,0.07780933380126953,0.4269611715,0.0,0.07069206237792969,0.6568596526,0.0,0.5093770027160645,0.0064154892,0.2083333333,0.1596219539642334,0.0145949606,0.0,0.1717538833618164,0.4627087621,0.2777777778,3.9493560791015625,0.8319607743,0.2777777778,4.122975826263428,0.9056488079,0.2777777778,0.5248630046844482,0.2534812171,0.0,0.41993212699890137,0.6261643669,0.0,0.43370485305786133,0.0040020039,0.2222222222,0.5505270957946777,1.58732e-05,0.1944444444,0.4008181095123291,0.2812434682,0.2777777778,0.4137399196624756,0.7710630055,0.2777777778,0.4801151752471924,0.7202058149,0.2777777778
+qpeinexact_indep_tket_4,qpeinexact,4,-1,0.9184317816,0.0,-1,2.38e-08,0.5490196078,0.055253028869628906,0.5372090193,0.0,0.04713177680969238,0.7709346309,0.0,0.21280789375305176,0.1212043267,0.0,0.24170923233032227,0.0826068811,0.0,0.16420316696166992,0.5161123277,0.0,3.9377830028533936,0.8553519636,0.0,4.089264154434204,0.9184317816,0.0,0.33877086639404297,0.468732408,0.0,0.2654881477355957,0.7849036525,0.0,0.2776949405670166,0.0976529068,0.0833333333,0.3739356994628906,0.0050412934,0.0,0.27278614044189453,0.4717268866,0.0,0.2833530902862549,0.8530641075,0.0,0.2773888111114502,0.8868511814,0.0
+dj_indep_qiskit_23,dj,23,-1,0.8460729175,0.0,-1,0.0,0.5031055901,0.26257896423339844,0.1594925675,0.1194029851,0.23286199569702148,0.2892698004,0.0175438596,-1,-1,-1,0.3930680751800537,1.026e-06,0.1111111111,-1,-1,-1,9.401432991027832,0.7941028981,0.0,9.420400857925415,0.8460729175,0.0,1.4212801456451416,0.0978595168,0.3305084746,1.4139721393585205,0.0467310787,0.5483870968,-1,-1,-1,1.4432902336120605,0.0,0.4462809917,-1,-1,-1,0.703624963760376,0.7330376341,0.0,0.7880229949951172,0.6850555819,0.0
+random_indep_tket_6,random,6,-1,0.7639393005,0.3181818182,-1,9.43267e-05,0.4705882353,0.17966008186340332,0.210502399,0.1794871795,0.16756391525268555,0.484842451,0.1794871795,0.8438291549682617,0.0002375223,0.3493975904,0.31868624687194824,4.17e-07,0.2535211268,5.114866018295288,0.1317600693,0.3488372093,5.125967741012573,0.6263505488,0.3488372093,5.970226049423218,0.7639393005,0.3181818182,1.206617832183838,0.3385890741,0.1951219512,1.1769599914550781,0.3198357918,0.164556962,1.0558481216430664,0.0002342582,0.3092783505,1.0828359127044678,1.993e-07,0.3134328358,0.9867820739746094,0.0700097394,0.375,0.9999449253082275,0.5750260215,0.375,1.082144021987915,0.5773163339,0.4
+graphstate_indep_tket_17,graphstate,17,-1,0.9184262563,0.7058823529,-1,0.0007783589,0.7553956835,0.10274195671081543,0.3972260038,0.725,0.0849771499633789,0.3914290389,0.7380952381,-1,-1,-1,0.13294005393981934,0.0007958362,0.6571428571,-1,-1,-1,7.475311756134033,0.8396858844,0.7058823529,7.5180559158325195,0.9184262563,0.7058823529,1.0937659740447998,0.1528655664,0.5930232558,0.9845976829528809,0.2950210877,0.5471698113,-1,-1,-1,0.9529311656951904,5.54961e-05,0.6818181818,-1,-1,-1,0.5433230400085449,0.7637258653,0.7058823529,0.657599925994873,0.6933403614,0.7058823529
+wstate_indep_qiskit_19,wstate,19,-1,0.774733737,0.4444444444,-1,0.0005204866,0.5181818182,0.8939521312713623,0.2557230606,0.4444444444,0.0926218032836914,0.4322048704,0.4444444444,-1,-1,-1,8.655577898025513,0.0008098191,0.4444444444,-1,-1,-1,8.320124864578247,0.6806253085,0.4444444444,8.508648872375488,0.774733737,0.4444444444,1.1054677963256836,0.1687921046,0.4444444444,1.0691659450531006,0.3872976751,0.4444444444,-1,-1,-1,1.0919523239135742,2.34239e-05,0.4444444444,-1,-1,-1,1.0592079162597656,0.5806685886,0.4444444444,1.1575706005096436,0.4928935631,0.4444444444
+vqe_indep_tket_16,vqe,16,-1,0.7794125192,0.4333333333,-1,0.0008086337,0.5945017182,0.4231879711151123,0.2872961994,0.4333333333,0.09523415565490723,0.4319337816,0.4333333333,-1,-1,-1,5.718792915344238,0.0015143608,0.4333333333,-1,-1,-1,7.1161229610443115,0.7168500726,0.4333333333,7.1540820598602295,0.7794125192,0.4333333333,1.0123980045318604,0.2857813704,0.4333333333,0.976870059967041,0.4793567057,0.4333333333,-1,-1,-1,0.9880249500274658,0.0010582918,0.4333333333,-1,-1,-1,0.9281978607177734,0.6266823716,0.4333333333,1.115907907485962,0.5339762323,0.4333333333
+qnn_indep_tket_6,qnn,6,-1,0.7266174615,0.4615384615,-1,0.4920628958,0.46875,0.1993260383605957,0.0246880517,0.2195121951,0.17499589920043945,0.2597152543,0.1937984496,3.706357955932617,0.000108509,0.3467741935,0.36164093017578125,0.0,0.1946902655,5.762531280517578,0.0674057976,0.46875,6.503989934921265,0.5233334688,0.46875,17.25782012939453,0.7266174615,0.4615384615,1.3191759586334229,0.2238998847,0.3112582781,1.159365177154541,0.2785120095,0.3051948052,1.169433832168579,4.8689e-06,0.3243243243,1.3602590560913086,0.0,0.3700787402,1.0896320343017578,0.0205669867,0.46875,1.113840103149414,0.4365847956,0.46875,1.2577259540557861,0.5382592341,0.4285714286
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+wstate_indep_tket_70,wstate,70,-1,7.01e-08,0.366442953,-1,0.0,0.6136101499,96.63244414329529,0.0067852148,0.4855072464,-1,-1,-1,-1,-1,-1,43.553874254226685,0.0,0.4855072464,-1,-1,-1,-1,-1,-1,-1,-1,-1,4.341140985488892,0.0001608373,0.7047619048,-1,-1,-1,-1,-1,-1,4.441609144210815,0.0,0.7894736842,-1,-1,-1,-1,-1,-1,-1,-1,-1
+graphstate_indep_qiskit_19,graphstate,19,-1,0.9092777426,0.7368421053,-1,0.0010874698,0.7750865052,0.28554511070251465,0.1867669791,0.5094339623,0.08771920204162598,0.4212394291,0.6666666667,-1,-1,-1,0.24365901947021484,9.24539e-05,0.6511627907,-1,-1,-1,8.339462041854858,0.8229886355,0.7368421053,8.530626773834229,0.9092777426,0.7368421053,1.0034840106964111,0.2435969944,0.5636363636,1.1964671611785889,0.3613541667,0.5652173913,-1,-1,-1,1.1067347526550293,0.0004480739,0.6486486486,-1,-1,-1,0.6376593112945557,0.7390169568,0.7368421053,0.698603630065918,0.6621499158,0.7368421053
+realamprandom_indep_tket_40,realamprandom,40,-1,-1,-1,-1,0.0,0.8400109582,19.624032974243164,0.0,0.8069632495,-1,-1,-1,-1,-1,-1,25.260831117630005,0.0,0.7986900147,-1,-1,-1,-1,-1,-1,-1,-1,-1,67.60584092140198,0.0,0.7876790579,-1,-1,-1,-1,-1,-1,70.4667661190033,0.0,0.79863174,-1,-1,-1,-1,-1,-1,-1,-1,-1
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+vqe_indep_tket_4,vqe,4,-1,0.9483377481,0.1666666667,-1,0.8592160978,0.1666666667,0.04585003852844238,0.826074105,0.1666666667,0.03298783302307129,0.8691424658,0.1666666667,0.05450892448425293,0.4789260458,0.1666666667,0.06263518333435059,0.3696942741,0.1666666667,0.13035202026367188,0.7429954403,0.1666666667,3.38246488571167,0.9347670839,0.1666666667,3.4194412231445312,0.9483377481,0.1666666667,0.18292713165283203,0.8582481366,0.1666666667,0.15710830688476562,0.7982525204,0.1666666667,0.1744861602783203,0.3727514022,0.1666666667,0.17766237258911133,0.4739441235,0.1666666667,0.15443134307861328,0.6184779258,0.1666666667,0.16648006439208984,0.9089515466,0.1666666667,0.19176983833312988,0.8780224717,0.1666666667
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+wstate_indep_tket_27,wstate,27,-1,0.6925741025,0.4615384615,-1,0.0,0.6071428571,5.3035361766815186,0.1641361537,0.4615384615,0.18799996376037598,0.1970562276,0.2235294118,-1,-1,-1,10.839931011199951,1.6187e-05,0.4615384615,-1,-1,-1,-1,-1,-1,10.319958686828613,0.6925741025,0.4615384615,1.6031959056854248,0.1831982895,0.4615384615,3.1545588970184326,0.1395512276,0.3559322034,-1,-1,-1,1.5725529193878174,1.214e-07,0.4615384615,-1,-1,-1,-1,-1,-1,1.6633708477020264,0.3607153063,0.4615384615
+qpeinexact_indep_qiskit_9,qpeinexact,9,-1,0.6411609114,0.4642857143,-1,0.0004742522,0.5,0.27415013313293457,0.0123807001,0.226993865,0.23172879219055176,0.1901188928,0.303030303,-1,-1,-1,0.4715702533721924,0.0,0.2876712329,9.200340032577515,0.0275182037,0.4642857143,9.243260860443115,0.424012154,0.4642857143,9.212133884429932,0.6411609114,0.4642857143,2.245081901550293,0.0956248492,0.2988505747,1.6778831481933594,0.1617051334,0.3216374269,-1,-1,-1,1.836082935333252,0.0,0.3801169591,1.3959081172943115,0.0130309944,0.4166666667,1.4024131298065186,0.3953758876,0.4166666667,1.2726709842681885,0.5176595417,0.4166666667
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+vqe_indep_tket_8,vqe,8,-1,0.8921694926,0.3571428571,-1,0.4381091994,0.3870967742,0.5628368854522705,0.6059806202,0.3571428571,0.30133485794067383,0.7591033372,0.3571428571,5.1901161670684814,0.148918574,0.3571428571,3.375411033630371,0.0848463395,0.3571428571,13.212748050689697,0.5052656696,0.3571428571,18.934125900268555,0.8557291037,0.3571428571,5.93096399307251,0.8921694926,0.3571428571,0.4251589775085449,0.7447973848,0.3571428571,0.3956770896911621,0.6442284464,0.3571428571,0.4315650463104248,0.1473002238,0.3571428571,0.42917919158935547,0.0248736586,0.3571428571,0.38762497901916504,0.330490736,0.3571428571,0.40103793144226074,0.8020258947,0.3571428571,0.45035696029663086,0.7429023189,0.3571428571
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+graphstate_indep_qiskit_15,graphstate,15,-1,0.9276668158,0.5333333333,-1,7.71e-08,0.619047619,1.2352583408355713,0.2944152251,0.5,0.0910043716430664,0.4436931344,0.3870967742,-1,-1,-1,0.21245098114013672,0.0053706356,0.5357142857,-1,-1,-1,15.999660015106201,0.8567218964,0.5333333333,10.33337688446045,0.9276668158,0.5333333333,0.9179120063781738,0.2377639212,0.2291666667,0.978938102722168,0.5364867737,0.5185185185,-1,-1,-1,1.0140888690948486,7.37503e-05,0.4166666667,-1,-1,-1,0.49925708770751953,0.7892609121,0.5333333333,0.5465209484100342,0.7260000269,0.5333333333
+qaoa_indep_tket_7,qaoa,7,-1,0.9258151947,0.5714285714,-1,0.005259977,0.6923076923,0.08287286758422852,0.4153466476,0.55,0.07329177856445312,0.4963974631,0.55,0.4351818561553955,0.0267288445,0.55,0.13862085342407227,0.0007604901,0.4705882353,4.478165149688721,0.5581678872,0.5714285714,4.526655912399292,0.8688381847,0.5714285714,4.504500150680542,0.9258151947,0.5714285714,0.7266807556152344,0.3266683357,0.55,0.6762099266052246,0.5084404401,0.5813953488,0.6149697303771973,0.0200512755,0.5813953488,0.7146241664886475,0.0487158907,0.55,0.49637889862060547,0.1782911457,0.5714285714,0.5115358829498291,0.6932158863,0.5714285714,0.45836496353149414,0.7670719303,0.5714285714
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+vqe_indep_qiskit_9,vqe,9,-1,0.8771180649,0.375,-1,0.1932914038,0.3970588235,2.2949378490448,0.582547767,0.375,0.7279257774353027,0.6229637927,0.375,-1,-1,-1,2.1928389072418213,0.0645378975,0.375,5.068168878555298,0.4576307238,0.375,42.58964204788208,0.8367019346,0.375,8.60168194770813,0.8771180649,0.375,0.5304388999938965,0.6989195047,0.375,0.49243998527526855,0.6258723217,0.375,-1,-1,-1,0.4855470657348633,0.0224722427,0.375,0.4466559886932373,0.2855421156,0.375,0.5799422264099121,0.7785657863,0.375,0.5525410175323486,0.7144685921,0.375
+dj_indep_qiskit_25,dj,25,-1,0.8334652635,0.0,-1,0.0016767638,0.4319727891,4.80794620513916,0.0870326927,0.0675675676,0.2811238765716553,0.290607182,0.1176470588,-1,-1,-1,13.837202787399292,2.9404e-06,0.0298507463,-1,-1,-1,0.34033203125,0.7786235215,0.0,10.366124629974365,0.8334652635,0.0,1.5113451480865479,0.1293156035,0.3963963964,1.4441609382629395,0.0508521925,0.4619047619,-1,-1,-1,1.3763530254364014,0.0,0.3137254902,-1,-1,-1,0.8183150291442871,0.7127351979,0.0,0.8585131168365479,0.6621399571,0.0
+qpeinexact_indep_tket_2,qpeinexact,2,-1,0.9870717764,0.0,-1,0.9606352705,0.0,0.023351669311523438,0.9568938315,0.0,0.06379914283752441,0.9697695773,0.0,6.816439867019653,0.8641473373,0.0,0.02620697021484375,0.4697571121,0.0,0.015865087509155273,0.9057229717,0.0,0.02395796775817871,0.977764962,0.0,0.02903127670288086,0.9870717764,0.0,0.07048487663269043,0.9675110774,0.0,0.04495811462402344,0.9412044126,0.0,0.14493227005004883,0.7109153643,0.0,0.08301973342895508,0.6841707287,0.0,0.045411109924316406,0.8685342282,0.0,0.05881786346435547,0.9715260044,0.0,0.1254138946533203,0.9752947789,0.0
+graphstate_indep_tket_11,graphstate,11,-1,0.9464277865,0.5454545455,-1,4.59322e-05,0.6753246753,1.228450059890747,0.4405758746,0.5833333333,6.90145206451416,0.5732961268,0.5833333333,-1,-1,-1,0.540996789932251,0.006252629,0.4761904762,0.07007789611816406,0.6258603106,0.5454545455,5.865082025527954,0.8939812514,0.5454545455,5.879359006881714,0.9464277865,0.5454545455,0.738095760345459,0.5951311958,0.5,0.750363826751709,0.6026579081,0.6470588235,-1,-1,-1,0.7335481643676758,0.0012178676,0.45,0.3686530590057373,0.4093708461,0.5454545455,0.40393805503845215,0.8378774426,0.5454545455,0.45830202102661133,0.7841502204,0.5454545455
+qpeinexact_indep_qiskit_30,qpeinexact,30,-1,0.0157695753,0.8055555556,-1,2.656e-07,0.8125,40.73461890220642,0.0,0.6881014873,-1,-1,-1,-1,-1,-1,15.734458208084106,0.0,0.656641604,-1,-1,-1,-1,-1,-1,132.71074199676514,0.0103059917,0.8125,21.24106192588806,0.0,0.6939040208,-1,-1,-1,-1,-1,-1,18.075438976287842,0.0,0.6301169591,-1,-1,-1,-1,-1,-1,13.829258918762207,0.000553052,0.8068965517
+realamprandom_indep_tket_50,realamprandom,50,-1,0.0,0.8117893477,-1,0.0,0.8697927793,51.009692907333374,0.0,0.8472974303,-1,-1,-1,-1,-1,-1,127.10829186439514,0.0,0.8360739542,-1,-1,-1,-1,-1,-1,-1,-1,-1,117.86021709442139,0.0,0.7503505677,-1,-1,-1,-1,-1,-1,121.69309687614441,0.0,0.7974668969,-1,-1,-1,-1,-1,-1,-1,-1,-1
+vqe_indep_tket_10,vqe,10,-1,0.858876452,0.3888888889,-1,0.132210168,0.5379310345,2.9079411029815674,0.4529711234,0.3888888889,0.06857919692993164,0.7064459661,0.3888888889,-1,-1,-1,4.163303852081299,0.0553373505,0.3888888889,109.26275730133057,0.4166646078,0.3888888889,91.43193197250366,0.8187527065,0.3888888889,41.44092798233032,0.858876452,0.3888888889,0.5874829292297363,0.6699678556,0.3888888889,0.6462790966033936,0.5987190809,0.3888888889,-1,-1,-1,0.7133769989013672,0.0131394804,0.3888888889,0.6462860107421875,0.2428582655,0.3888888889,0.6586906909942627,0.7539798234,0.3888888889,0.773273229598999,0.683694382,0.3888888889
+wstate_indep_tket_60,wstate,60,-1,0.000144303,0.4678492239,-1,0.0,0.5077844311,60.66488599777222,0.0423787655,0.4830508475,-1,-1,-1,-1,-1,-1,50.93488383293152,5e-10,0.4830508475,-1,-1,-1,-1,-1,-1,-1,-1,-1,3.7018489837646484,0.0041096268,0.5602409639,-1,-1,-1,-1,-1,-1,3.748763084411621,0.0,0.6165803109,-1,-1,-1,-1,-1,-1,-1,-1,-1
+qpeinexact_indep_tket_60,qpeinexact,60,-1,0.0,0.5994330897,-1,0.0,0.829509534,22.62827706336975,0.0,0.7623599209,-1,-1,-1,-1,-1,-1,31.314715147018433,0.0,0.7833743364,-1,-1,-1,-1,-1,-1,-1,-1,-1,65.11338210105896,0.0,0.7309956273,-1,-1,-1,-1,-1,-1,65.38992476463318,0.0,0.7224664992,-1,-1,-1,-1,-1,-1,-1,-1,-1
+qftentangled_indep_qiskit_12,qftentangled,12,-1,0.4102741895,0.7080745342,-1,1.104e-07,0.7237851662,12.52927017211914,0.0005267832,0.4896755162,35.82759213447571,0.04385855,0.5088235294,-1,-1,-1,85.63940691947937,0.0,0.4668874172,-1,-1,-1,149.05579280853271,0.193420913,0.7080745342,98.71565961837769,0.4102741895,0.7080745342,13.632351160049438,0.0022619917,0.5331491713,3.6368887424468994,0.0205285081,0.5376044568,-1,-1,-1,14.9406578540802,0.0,0.6448275862,-1,-1,-1,3.0265791416168213,0.1562034236,0.6923076923,2.6796460151672363,0.2458747337,0.7012987013
+qftentangled_indep_tket_5,qftentangled,5,-1,0.8351196252,0.3666666667,-1,1.20997e-05,0.3717948718,0.9963667392730713,0.5709552168,0.1224489796,0.5833871364593506,0.6521758537,0.1224489796,87.09295725822449,0.0027405994,0.2407407407,4.775393009185791,0.013789049,0.3684210526,7.702020645141602,0.2684741948,0.3666666667,39.01664400100708,0.7325056368,0.3666666667,34.60891008377075,0.8351196252,0.3666666667,0.815004825592041,0.5825189378,0.1282051282,1.4373221397399902,0.5824590108,0.1282051282,0.7766571044921875,0.0082807957,0.2916666667,0.7254188060760498,4.50876e-05,0.3055555556,0.6976208686828613,0.2173396734,0.3333333333,0.6611299514770508,0.7251027731,0.3333333333,0.6545579433441162,0.7601958332,0.3571428571
+qftentangled_indep_qiskit_5,qftentangled,5,-1,0.8351196252,0.3666666667,-1,2.50149e-05,0.4414414414,0.8246300220489502,0.5709552168,0.1224489796,0.5675721168518066,0.6521758537,0.1224489796,86.70621800422668,0.0027405994,0.2407407407,3.7728629112243652,0.013789049,0.3684210526,8.803516149520874,0.2684741948,0.3666666667,39.07728314399719,0.7325056368,0.3666666667,32.419520139694214,0.8351196252,0.3666666667,0.8319590091705322,0.5825189378,0.1282051282,0.852902889251709,0.5824590108,0.1282051282,0.7904829978942871,0.0082807957,0.2916666667,1.3902549743652344,4.50876e-05,0.3055555556,0.5377678871154785,0.2173396734,0.3333333333,0.7130012512207031,0.7251027731,0.3333333333,0.670330286026001,0.7601958332,0.3571428571
+graphstate_indep_tket_50,graphstate,50,-1,0.0050309175,0.8349056604,-1,0.0,0.8592162554,30.282397985458374,0.003459796,0.7777777778,-1,-1,-1,-1,-1,-1,265.6690192222595,1.85623e-05,0.86,-1,-1,-1,-1,-1,-1,-1,-1,-1,3.722043991088867,0.0073237946,0.7043478261,-1,-1,-1,-1,-1,-1,1.8061728477478027,2.032e-07,0.86,-1,-1,-1,-1,-1,-1,-1,-1,-1
+realamprandom_indep_tket_11,realamprandom,11,-1,0.5342937316,0.7515151515,-1,0.000377835,0.7515151515,88.71078896522522,0.0028193009,0.5914489311,61.550830125808716,0.0065970594,0.6224489796,-1,-1,-1,54.375279903411865,0.0,0.5846560847,0.2684788703918457,0.0017182336,0.7515151515,90.4616949558258,0.2060086979,0.7515151515,69.53314423561096,0.5342937316,0.7515151515,17.29090905189514,8.219e-07,0.6319758673,5.428009986877441,0.0003675788,0.5622119816,-1,-1,-1,17.53189182281494,0.0,0.6156462585,3.894711971282959,2.08824e-05,0.7515151515,3.9193480014801025,0.1050654737,0.7515151515,4.4664740562438965,0.0691946512,0.7515151515
+wstate_indep_tket_21,wstate,21,-1,0.7533224554,0.45,-1,0.0,0.5049833887,8.404181003570557,0.2926006569,0.45,1.8473410606384277,0.3831527265,0.45,-1,-1,-1,133.08268904685974,0.0008946343,0.45,-1,-1,-1,54.26053285598755,0.6522582035,0.45,44.37506604194641,0.7533224554,0.45,1.2981250286102295,0.2414951667,0.45,1.2199809551239014,0.354206723,0.45,-1,-1,-1,1.2639870643615723,1.18627e-05,0.45,-1,-1,-1,1.211388111114502,0.5467574098,0.45,1.3646531105041504,0.4558857347,0.45
+tsp_indep_tket_9,tsp,9,-1,0.795141735,0.6,-1,0.3016270275,0.6,3.777235984802246,0.2971279431,0.6,2.530111074447632,0.4818137687,0.6,-1,-1,-1,2.370059013366699,0.0025353843,0.6,37.35679316520691,0.1688635037,0.6,37.632577657699585,0.657761354,0.6,37.312435150146484,0.795141735,0.6,1.3846142292022705,0.529253429,0.6,1.279332160949707,0.4522166463,0.6,-1,-1,-1,1.3713200092315674,0.0001994647,0.6,1.2069501876831055,0.0551566566,0.6,1.249953031539917,0.5544673996,0.6,1.402353048324585,0.4508959903,0.6
diff --git a/src/mqt/predictor/ml/training_data/trained_model/res_GHZ.csv b/src/mqt/predictor/ml/training_data/trained_model/res_GHZ.csv
new file mode 100644
index 000000000..c5f42d034
--- /dev/null
+++ b/src/mqt/predictor/ml/training_data/trained_model/res_GHZ.csv
@@ -0,0 +1,71 @@
+file_path,benchmark_name,num_qubits,MQTPredictor_expected_fidelity_time,MQTPredictor_expected_fidelity_expected_fidelity,MQTPredictor_expected_fidelity_critical_depth,MQTPredictor_critical_depth_time,MQTPredictor_critical_depth_expected_fidelity,MQTPredictor_critical_depth_critical_depth,qiskit_ibm_washington_time,qiskit_ibm_washington_expected_fidelity,qiskit_ibm_washington_critical_depth,qiskit_ibm_montreal_time,qiskit_ibm_montreal_expected_fidelity,qiskit_ibm_montreal_critical_depth,qiskit_oqc_lucy_time,qiskit_oqc_lucy_expected_fidelity,qiskit_oqc_lucy_critical_depth,qiskit_rigetti_aspen_m2_time,qiskit_rigetti_aspen_m2_expected_fidelity,qiskit_rigetti_aspen_m2_critical_depth,qiskit_ionq_harmony_time,qiskit_ionq_harmony_expected_fidelity,qiskit_ionq_harmony_critical_depth,qiskit_ionq_aria1_time,qiskit_ionq_aria1_expected_fidelity,qiskit_ionq_aria1_critical_depth,qiskit_quantinuum_h2_time,qiskit_quantinuum_h2_expected_fidelity,qiskit_quantinuum_h2_critical_depth,tket_ibm_washington_time,tket_ibm_washington_expected_fidelity,tket_ibm_washington_critical_depth,tket_ibm_montreal_time,tket_ibm_montreal_expected_fidelity,tket_ibm_montreal_critical_depth,tket_oqc_lucy_time,tket_oqc_lucy_expected_fidelity,tket_oqc_lucy_critical_depth,tket_rigetti_aspen_m2_time,tket_rigetti_aspen_m2_expected_fidelity,tket_rigetti_aspen_m2_critical_depth,tket_ionq_harmony_time,tket_ionq_harmony_expected_fidelity,tket_ionq_harmony_critical_depth,tket_ionq_aria1_time,tket_ionq_aria1_expected_fidelity,tket_ionq_aria1_critical_depth,tket_quantinuum_h2_time,tket_quantinuum_h2_expected_fidelity,tket_quantinuum_h2_critical_depth
+ghz_indep_qiskit_9,ghz,9,-1,0.9286149641,0.0,-1,0.653471743,0.0,0.4457237720489502,0.757242533,0.0,0.48350000381469727,0.6825986022,0.0,-1,-1,-1,1.4558279514312744,0.2090489225,0.0,22.99870491027832,0.6729480382,0.0,35.85166096687317,0.9139825023,0.0,36.91637682914734,0.9286149641,0.0,0.33574771881103516,0.7678648995,0.0,0.27324795722961426,0.6981867621,0.0,-1,-1,-1,0.538517951965332,0.1087453877,0.0,0.3094508647918701,0.51107968,0.0,0.299332857131958,0.8763828378,0.0,0.4588918685913086,0.8310073612,0.0
+ghz_indep_tket_90,ghz,90,-1,1.457e-06,0.0281456954,-1,2e-10,0.4527027027,54.508131980895996,0.0308423306,0.0,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,3.1953001022338867,0.0186654502,0.0,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1
+ghz_indep_tket_28,ghz,28,-1,0.7825450407,0.0,-1,0.0,0.4315352697,46.509843826293945,0.2894075296,0.0,-1,-1,-1,-1,-1,-1,72.34988212585449,0.0011604036,0.0,-1,-1,-1,-1,-1,-1,30.186613082885742,0.7825450407,0.0,0.8294031620025635,0.2823738904,0.0,-1,-1,-1,-1,-1,-1,0.8820011615753174,4.42977e-05,0.0,-1,-1,-1,-1,-1,-1,0.8762640953063965,0.5366589847,0.0
+ghz_indep_tket_12,ghz,12,-1,0.9038574474,0.0,-1,0.5288333005,0.0,0.605618953704834,0.574380864,0.0,0.34788012504577637,0.6515634006,0.0,-1,-1,-1,5.6179358959198,0.1592257577,0.0,-1,-1,-1,44.01083493232727,0.8836689334,0.0,42.14278793334961,0.9038574474,0.0,0.5164949893951416,0.717963403,0.0,0.43810224533081055,0.6360000207,0.0,-1,-1,-1,0.43369102478027344,0.0502558185,0.0,-1,-1,-1,0.4335291385650635,0.8341981464,0.0,0.4345879554748535,0.7755675672,0.0
+ghz_indep_qiskit_15,ghz,15,-1,0.8797599833,0.0,-1,0.1702397738,0.4337349398,1.4662468433380127,0.528643537,0.0,0.2423839569091797,0.5317553549,0.0,-1,-1,-1,19.02910804748535,0.030589607,0.0,-1,-1,-1,50.53348779678345,0.8543607584,0.0,49.16796088218689,0.8797599833,0.0,0.5518379211425781,0.4776174386,0.0,0.5222020149230957,0.6025023764,0.0,-1,-1,-1,0.6343259811401367,0.0258034764,0.0,-1,-1,-1,0.4066612720489502,0.7940440152,0.0,0.48864316940307617,0.7238263814,0.0
+ghz_indep_qiskit_5,ghz,5,-1,0.9626830542,0.0,-1,0.2272388856,0.0,0.4353139400482178,0.87527228,0.0,0.3628580570220947,0.8795632899,0.0,0.7759947776794434,0.4910021899,0.0,0.30077195167541504,0.4097438145,0.0,0.8022871017456055,0.8203341016,0.0,5.626266241073608,0.956024321,0.0,3.675267219543457,0.9626830542,0.0,0.16539335250854492,0.8647845818,0.0,0.14389991760253906,0.8227553618,0.0,0.13814377784729004,0.3794972777,0.0,0.647219181060791,0.3384438028,0.0,0.14530706405639648,0.7139623218,0.0,0.2284841537475586,0.9359659634,0.0,0.23812603950500488,0.9111401395,0.0
+ghz_indep_qiskit_23,ghz,23,-1,0.818594656,0.0,-1,3.2708e-06,0.0,2.4350640773773193,0.3855864085,0.0,0.09367513656616211,0.3446515098,0.0,-1,-1,-1,20.940719842910767,0.0025605233,0.0,-1,-1,-1,30.612523078918457,0.7808707839,0.0,63.34886193275452,0.818594656,0.0,0.7905921936035156,0.3260630688,0.0,1.8583612442016602,0.2836743317,0.0,-1,-1,-1,0.8897249698638916,4.25446e-05,0.0,-1,-1,-1,0.8128681182861328,0.6961650134,0.0,1.0989677906036377,0.6021071584,0.0
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+ghz_indep_tket_6,ghz,6,-1,0.9540506127,0.0,-1,0.4733813897,0.0,0.07920479774475098,0.8582598,0.0,0.15844297409057617,0.8212437755,0.0,1.5134708881378174,0.378949643,0.0,0.5415999889373779,0.3287613141,0.0,19.078651189804077,0.7807076898,0.0,15.619247913360596,0.9453359544,0.0,9.198162078857422,0.9540506127,0.0,0.19332122802734375,0.8337568618,0.0,0.14092302322387695,0.7687568315,0.0,0.13526177406311035,0.3442143123,0.0,0.16996479034423828,0.2178108218,0.0,0.134490966796875,0.6567175515,0.0,0.18168187141418457,0.9207007733,0.0,0.25952696800231934,0.8904101508,0.0
+ghz_indep_qiskit_2,ghz,2,-1,0.9890518602,0.0,-1,0.9663620251,0.0,0.019566059112548828,0.9569614219,0.0,0.009910106658935547,0.9451842582,0.0,0.06618499755859375,0.8538801855,0.0,0.02289724349975586,0.5953072816,0.0,0.010795116424560547,0.9516947892,0.0,0.019875049591064453,0.9888199846,0.0,0.024904966354370117,0.9890518602,0.0,0.054419755935668945,0.9372896691,0.0,0.02405095100402832,0.9346289213,0.0,0.021207094192504883,0.6987311033,0.0,0.04189610481262207,0.7696699883,0.0,0.023729801177978516,0.9174138715,0.0,0.06458902359008789,0.9832969669,0.0,0.054747819900512695,0.9762710499,0.0
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+ghz_indep_qiskit_70,ghz,70,-1,0.0,0.4952727273,-1,0.0,0.4696569921,58.59254288673401,0.0377032421,0.0,-1,-1,-1,-1,-1,-1,37.894572734832764,1.557e-07,0.0,-1,-1,-1,-1,-1,-1,-1,-1,-1,2.1562159061431885,0.0203382407,0.0,-1,-1,-1,-1,-1,-1,2.5026090145111084,5e-10,0.0,-1,-1,-1,-1,-1,-1,-1,-1,-1
+ghz_indep_qiskit_27,ghz,27,-1,0.7896256653,0.0,-1,3e-10,0.4090909091,9.42116403579712,0.3141096745,0.0,0.09851980209350586,0.2846899925,0.0,-1,-1,-1,11.38270902633667,0.0014729035,0.0,-1,-1,-1,-1,-1,-1,10.145493984222412,0.7896256653,0.0,0.8174116611480713,0.288050714,0.0,2.405587911605835,0.2565514272,0.0483870968,-1,-1,-1,0.8771581649780273,6.80802e-05,0.0,-1,-1,-1,-1,-1,-1,0.8786873817443848,0.5491531535,0.0
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+ghz_indep_tket_16,ghz,16,-1,0.8718711184,0.0,-1,0.0386645888,0.3783783784,1.1915748119354248,0.5003202841,0.0,0.10942506790161133,0.5227478001,0.0,-1,-1,-1,23.268949031829834,0.0533145392,0.0,-1,-1,-1,28.12800097465515,0.844808992,0.0,35.976017236709595,0.8718711184,0.0,0.9159550666809082,0.3807657009,0.0,0.43377113342285156,0.5785845596,0.0,-1,-1,-1,0.48523378372192383,0.0194405358,0.0,-1,-1,-1,0.43734216690063477,0.7810935091,0.0,0.6265237331390381,0.7073580995,0.0
+ghz_indep_qiskit_50,ghz,50,-1,5.6747e-06,0.449725777,-1,0.0,0.4273127753,58.71120476722717,0.1438376541,0.0,-1,-1,-1,-1,-1,-1,43.422255754470825,1.29924e-05,0.0,-1,-1,-1,-1,-1,-1,-1,-1,-1,2.7848100662231445,0.1133961078,0.0,-1,-1,-1,-1,-1,-1,2.122075080871582,5.487e-07,0.0,-1,-1,-1,-1,-1,-1,-1,-1,-1
+ghz_indep_tket_9,ghz,9,-1,0.9286149641,0.0,-1,0.653471743,0.0,0.2641580104827881,0.757242533,0.0,0.2897369861602783,0.6825986022,0.0,-1,-1,-1,1.3931350708007812,0.2090489225,0.0,18.09169578552246,0.6729480382,0.0,28.828248977661133,0.9139825023,0.0,24.402803897857666,0.9286149641,0.0,0.3661000728607178,0.7678648995,0.0,0.24258804321289062,0.6981867621,0.0,-1,-1,-1,0.284257173538208,0.1087453877,0.0,0.24422001838684082,0.51107968,0.0,0.6917049884796143,0.8763828378,0.0,0.37233400344848633,0.8310073612,0.0
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+ghz_indep_tket_4,ghz,4,-1,0.9713936037,0.0,-1,0.8938413911,0.0,0.4174058437347412,0.8886827238,0.0,0.997183084487915,0.9008860046,0.0,0.047254085540771484,0.5747547306,0.0,0.03577280044555664,0.5578367272,0.0,0.11339735984802246,0.8619718326,0.0,22.708863973617554,0.9668335347,0.0,10.30822229385376,0.9713936037,0.0,0.12598824501037598,0.8945129678,0.0,0.0826869010925293,0.8415276188,0.0,0.7749209403991699,0.4666624777,0.0,0.10613203048706055,0.6400099817,0.0,0.08336424827575684,0.7761970055,0.0,0.09679317474365234,0.9514842499,0.0,0.11911296844482422,0.9323527513,0.0
+ghz_indep_tket_60,ghz,60,-1,5.0149e-06,0.0,-1,0.0,0.4699029126,249.3824462890625,0.1319701455,0.0,-1,-1,-1,-1,-1,-1,49.97502326965332,3.1088e-06,0.0,-1,-1,-1,-1,-1,-1,-1,-1,-1,1.870650291442871,0.0655033361,0.0,-1,-1,-1,-1,-1,-1,2.048123836517334,4e-09,0.0,-1,-1,-1,-1,-1,-1,-1,-1,-1
+ghz_indep_qiskit_26,ghz,26,-1,0.7967703568,0.0,-1,0.0,0.4777517564,30.312323808670044,0.3367980748,0.0,4.620943069458008,0.3138322321,0.0,-1,-1,-1,69.45200204849243,0.0015555679,0.0,-1,-1,-1,-1,-1,-1,18.676201820373535,0.7967703568,0.0,1.2020201683044434,0.3052336621,0.0,1.9697470664978027,0.2051238063,0.1463414634,-1,-1,-1,0.8610198497772217,0.0001158822,0.0,-1,-1,-1,-1,-1,-1,0.9975278377532959,0.5619382039,0.0
+ghz_indep_tket_21,ghz,21,-1,0.8334752918,0.0,-1,5.784e-07,0.4903846154,3.207396984100342,0.4565763984,0.0,0.5216000080108643,0.4801551903,0.0,-1,-1,-1,47.187438011169434,0.015528276,0.0,-1,-1,-1,65.62262463569641,0.7986283148,0.0,62.94452095031738,0.8334752918,0.0,0.6071979999542236,0.3551920047,0.0,0.5752618312835693,0.4452309211,0.0,-1,-1,-1,1.0506598949432373,0.0010313884,0.0,-1,-1,-1,0.6564249992370605,0.7194411744,0.0,0.7859869003295898,0.6304693013,0.0
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+ghz_indep_qiskit_21,ghz,21,-1,0.8334752918,0.0,-1,9.16022e-05,0.0,8.526052236557007,0.4565763984,0.0,0.5597820281982422,0.4801551903,0.0,-1,-1,-1,70.78665399551392,0.015528276,0.0,-1,-1,-1,62.03017592430115,0.7986283148,0.0,20.57566475868225,0.8334752918,0.0,0.6652660369873047,0.3551920047,0.0,0.6212286949157715,0.4452309211,0.0,-1,-1,-1,0.6989879608154297,0.0010313884,0.0,-1,-1,-1,0.7650790214538574,0.7194411744,0.0,0.8139300346374512,0.6304693013,0.0
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+ghz_indep_qiskit_7,ghz,7,-1,0.9454955789,0.0,-1,0.8030171353,0.0,0.3627288341522217,0.58823843,0.0,0.6513590812683105,0.8068380206,0.0,2.4897079467773438,0.3147512692,0.0,0.6476490497589111,0.1509210987,0.0,22.142369747161865,0.7429954403,0.0,31.390631914138794,0.9347670839,0.0,29.07734704017639,0.9454955789,0.0,0.2884562015533447,0.8141898677,0.0,0.2855520248413086,0.7336130034,0.0,0.21552705764770508,0.3020880175,0.0,0.36634206771850586,0.1416040009,0.0,0.19481992721557617,0.6040626085,0.0,0.28978896141052246,0.9056845517,0.0,0.9632248878479004,0.8701518046,0.0
+ghz_indep_tket_30,ghz,30,-1,0.7685736994,0.0,-1,4e-10,0.0,17.963940143585205,0.2486362974,0.0,-1,-1,-1,-1,-1,-1,18.586599111557007,0.0005960292,0.0,-1,-1,-1,-1,-1,-1,50.66330599784851,0.7685736994,0.0,0.9801290035247803,0.2695361334,0.0,-1,-1,-1,-1,-1,-1,1.0785508155822754,2.9187e-05,0.0,-1,-1,-1,-1,-1,-1,0.9617209434509277,0.5125169705,0.0
+ghz_indep_qiskit_60,ghz,60,-1,0.001053608,0.0,-1,0.0,0.4547770701,219.91449689865112,0.1319701455,0.0,-1,-1,-1,-1,-1,-1,158.69014883041382,3.1088e-06,0.0,-1,-1,-1,-1,-1,-1,-1,-1,-1,1.855097770690918,0.0655033361,0.0,-1,-1,-1,-1,-1,-1,2.0263731479644775,4e-09,0.0,-1,-1,-1,-1,-1,-1,-1,-1,-1
+ghz_indep_qiskit_6,ghz,6,-1,0.9540506127,0.0,-1,0.4733813897,0.0,0.1384880542755127,0.8582598,0.0,0.13895773887634277,0.8212437755,0.0,2.7511439323425293,0.378949643,0.0,0.42139697074890137,0.3287613141,0.0,17.652101039886475,0.7807076898,0.0,16.270458936691284,0.9453359544,0.0,13.345922231674194,0.9540506127,0.0,0.21522092819213867,0.8337568618,0.0,0.16613292694091797,0.7687568315,0.0,0.14594507217407227,0.3442143123,0.0,0.2382359504699707,0.2178108218,0.0,0.1462421417236328,0.6567175515,0.0,0.19625306129455566,0.9207007733,0.0,0.2213146686553955,0.8904101508,0.0
+ghz_indep_tket_2,ghz,2,-1,0.9890518602,0.0,-1,0.9663620251,0.0,0.14385294914245605,0.9569614219,0.0,0.15895891189575195,0.9451842582,0.0,0.46231603622436523,0.8538801855,0.0,0.46508312225341797,0.5953072816,0.0,0.19759893417358398,0.9516947892,0.0,0.7724909782409668,0.9888199846,0.0,0.05513811111450195,0.9890518602,0.0,0.24077892303466797,0.9372896691,0.0,0.029822111129760742,0.9346289213,0.0,0.10738778114318848,0.6987311033,0.0,0.10691308975219727,0.7696699883,0.0,0.07299518585205078,0.9174138715,0.0,0.255145788192749,0.9832969669,0.0,0.19973230361938477,0.9762710499,0.0
+ghz_indep_qiskit_20,ghz,20,-1,0.8410167436,0.0,-1,0.0,0.4657039711,3.404294013977051,0.4164753197,0.0,0.5226180553436279,0.48828436,0.0,-1,-1,-1,45.0600049495697,0.0173368413,0.0,-1,-1,-1,46.438364028930664,0.807657943,0.0,35.484387159347534,0.8410167436,0.0,0.6718490123748779,0.4061385945,0.0,0.5961129665374756,0.4539367096,0.0,-1,-1,-1,0.6836211681365967,0.0012870996,0.0,-1,-1,-1,0.6775531768798828,0.7313694866,0.0,0.7386312484741211,0.645147505,0.0
+ghz_indep_tket_70,ghz,70,-1,2.42166e-05,0.0266075388,-1,0.0,0.4173998045,173.6547451019287,0.0377032421,0.0,-1,-1,-1,-1,-1,-1,62.11665177345276,1.557e-07,0.0,-1,-1,-1,-1,-1,-1,-1,-1,-1,2.1419129371643066,0.0203382407,0.0,-1,-1,-1,-1,-1,-1,2.5101478099823,5e-10,0.0,-1,-1,-1,-1,-1,-1,-1,-1,-1
+ghz_indep_tket_27,ghz,27,-1,0.7896256653,0.0,-1,0.0,0.458677686,31.220130920410156,0.3141096745,0.0,1.8231987953186035,0.2846899925,0.0,-1,-1,-1,56.25185489654541,0.0014729035,0.0,-1,-1,-1,-1,-1,-1,43.38505005836487,0.7896256653,0.0,0.8656790256500244,0.288050714,0.0,2.37599515914917,0.2565514272,0.0483870968,-1,-1,-1,0.9466629028320312,6.80802e-05,0.0,-1,-1,-1,-1,-1,-1,0.9448192119598389,0.5491531535,0.0
+ghz_indep_tket_50,ghz,50,-1,0.0009333262,0.0600706714,-1,0.0,0.4486486486,116.54977297782898,0.1438376541,0.0,-1,-1,-1,-1,-1,-1,79.87804102897644,1.29924e-05,0.0,-1,-1,-1,-1,-1,-1,-1,-1,-1,1.4579169750213623,0.1133961078,0.0,-1,-1,-1,-1,-1,-1,1.617940902709961,5.487e-07,0.0,-1,-1,-1,-1,-1,-1,-1,-1,-1
+ghz_indep_tket_11,ghz,11,-1,0.9120357311,0.0,-1,0.1583389377,0.4029850746,0.6571078300476074,0.687340312,0.0,0.6533927917480469,0.753645231,0.0,-1,-1,-1,2.9034857749938965,0.1754933918,0.0,0.38907527923583984,0.609504497,0.0,32.76772975921631,0.8936600667,0.0,27.28949499130249,0.9120357311,0.0,0.49712300300598145,0.7114299644,0.0,0.3600010871887207,0.6607167066,0.0,-1,-1,-1,0.381817102432251,0.0617298031,0.0,0.2984309196472168,0.4324095477,0.0,0.3883931636810303,0.8480291256,0.0,0.4543170928955078,0.7936238605,0.0
+ghz_indep_qiskit_16,ghz,16,-1,0.8718711184,0.0,-1,0.0646970453,0.4285714286,2.0538251399993896,0.5003202841,0.0,0.2976698875427246,0.5227478001,0.0,-1,-1,-1,32.78341794013977,0.0533145392,0.0,-1,-1,-1,41.35559701919556,0.844808992,0.0,32.25247406959534,0.8718711184,0.0,0.48841118812561035,0.3807657009,0.0,0.4621288776397705,0.5785845596,0.0,-1,-1,-1,0.6412169933319092,0.0194405358,0.0,-1,-1,-1,0.5123639106750488,0.7810935091,0.0,0.6251389980316162,0.7073580995,0.0
diff --git a/src/mqt/predictor/ml/training_data/trained_model/trained_clf.joblib b/src/mqt/predictor/ml/training_data/trained_model/trained_clf.joblib
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diff --git a/src/mqt/predictor/ml/training_data/training_data_aggregated/training_data_expected_fidelity.npy b/src/mqt/predictor/ml/training_data/training_data_aggregated/training_data_expected_fidelity.npy
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diff --git a/src/mqt/predictor/reward.py b/src/mqt/predictor/reward.py
index 2a66776bc..2f1be1c6a 100644
--- a/src/mqt/predictor/reward.py
+++ b/src/mqt/predictor/reward.py
@@ -1,7 +1,7 @@
from __future__ import annotations
import logging
-from typing import cast
+from typing import TYPE_CHECKING, Literal, cast
import numpy as np
from mqt.bench.utils import calc_qubit_index, calc_supermarq_features
@@ -9,173 +9,239 @@
from mqt.predictor.utils import (
get_rigetti_qubit_dict,
)
-from qiskit import QuantumCircuit
-logger = logging.getLogger("mqtpredictor")
+if TYPE_CHECKING:
+ from qiskit import QuantumCircuit
+
+logger = logging.getLogger("mqt-predictor")
+
+figure_of_merit = Literal["expected_fidelity", "critical_depth"]
def crit_depth(qc: QuantumCircuit, precision: int = 10) -> float:
+ """Calculates the critical depth of a given quantum circuit."""
supermarq_features = calc_supermarq_features(qc)
return cast(float, np.round(1 - supermarq_features.critical_depth, precision))
-def gate_ratio(qc: QuantumCircuit, precision: int = 10) -> float:
- return cast(float, np.round(1 - qc.num_nonlocal_gates() / qc.size(), precision))
+def expected_fidelity(qc: QuantumCircuit, device_name: str, precision: int = 10) -> float:
+ """Calculates the expected fidelity of a given quantum circuit on a given device.
+
+ Args:
+ qc (QuantumCircuit): The quantum circuit to be compiled.
+ device_name (str): The device to be used for compilation.
+ precision (int, optional): The precision of the returned value. Defaults to 10.
+
+ Returns:
+ float: The expected fidelity of the given quantum circuit on the given device.
+ """
+ if "ibm" in device_name:
+ res = calc_expected_fidelity_ibm(qc, device_name)
-def mix(qc: QuantumCircuit, device: str, precision: int = 10) -> float:
- return expected_fidelity(qc, device, precision) * 0.5 + crit_depth(qc, precision) * 0.5
+ elif "oqc_lucy" in device_name:
+ res = calc_expected_fidelity_oqc_lucy(qc)
+ elif "ionq" in device_name:
+ res = calc_expected_fidelity_ionq(qc, device_name)
+
+ elif "quantinuum_h2" in device_name:
+ res = calc_expected_fidelity_quantinuum_h2(qc)
+
+ elif "rigetti_aspen_m2" in device_name:
+ res = calc_expected_fidelity_rigetti_aspen_m2(qc)
-def expected_fidelity(qc_or_path: QuantumCircuit | str, device: str, precision: int = 10) -> float: # noqa: PLR0915
- if isinstance(qc_or_path, QuantumCircuit):
- qc = qc_or_path
else:
- try:
- qc = QuantumCircuit.from_qasm_file(qc_or_path)
- except Exception:
- raise RuntimeError("Could not read QuantumCircuit from: " + qc_or_path) from None
+ error_msg = "Device not supported"
+ raise ValueError(error_msg)
+
+ return cast(float, np.round(res, precision))
- res = 1.0
+def calc_expected_fidelity_rigetti_aspen_m2(qc: QuantumCircuit) -> float:
+ res = 1.0
calibration = Calibration.Calibration()
- if "ibm_montreal" in device or "ibm_washington" in device:
- if "ibm_montreal" in device:
- backend = calibration.ibm_montreal_calibration
- else:
- backend = calibration.ibm_washington_calibration
-
- for instruction, qargs, _cargs in qc.data:
- gate_type = instruction.name
-
- assert gate_type in ["rz", "sx", "x", "cx", "measure", "barrier"]
-
- if gate_type != "barrier":
- assert len(qargs) in [1, 2]
- first_qubit = calc_qubit_index(qargs, qc.qregs, 0)
- if len(qargs) == 1:
- try:
- if gate_type == "measure":
- specific_error: float = backend.readout_error(first_qubit)
- else:
- specific_error = backend.gate_error(gate_type, [first_qubit])
- except Exception as e:
- raise RuntimeError(
- "Error in IBM backend.gate_error(): "
- + str(e)
- + ", "
- + device
- + ", "
- + first_qubit
- + ", "
- + instruction
- + ", "
- + qargs
- ) from None
- else:
- second_qubit = calc_qubit_index(qargs, qc.qregs, 1)
- try:
- specific_error = backend.gate_error(gate_type, [first_qubit, second_qubit])
- if specific_error == 1:
- specific_error = calibration.ibm_washington_cx_mean_error
- except Exception as e:
- raise RuntimeError(
- "Error in IBM backend.gate_error(): "
- + str(e)
- + ", "
- + device
- + ", "
- + first_qubit
- + ", "
- + second_qubit
- + ", "
- + instruction
- + ", "
- + qargs
- ) from None
-
- res *= 1 - specific_error
- elif "oqc_lucy" in device:
- for instruction, qargs, _cargs in qc.data:
- gate_type = instruction.name
-
- assert gate_type in ["rz", "sx", "x", "ecr", "measure", "barrier"]
- if gate_type != "barrier":
- assert len(qargs) in [1, 2]
- first_qubit = calc_qubit_index(qargs, qc.qregs, 0)
- if len(qargs) == 1 and gate_type != "measure":
- specific_fidelity = calibration.oqc_lucy_calibration["fid_1Q"][str(first_qubit)]
- elif len(qargs) == 1 and gate_type == "measure":
- specific_fidelity = calibration.oqc_lucy_calibration["fid_1Q_readout"][str(first_qubit)]
- elif len(qargs) == 2:
- second_qubit = calc_qubit_index(qargs, qc.qregs, 1)
- tmp = str(first_qubit) + "-" + str(second_qubit)
- if calibration.oqc_lucy_calibration["fid_2Q"].get(tmp) is None:
- specific_fidelity = calibration.oqc_lucy_calibration["avg_2Q"]
- else:
- specific_fidelity = calibration.oqc_lucy_calibration["fid_2Q"][tmp]
-
- res *= specific_fidelity
-
- elif "ionq11" in device:
- for instruction, qargs, _cargs in qc.data:
- gate_type = instruction.name
-
- assert gate_type in ["rxx", "rz", "ry", "rx", "measure", "barrier"]
- if gate_type != "barrier":
- assert len(qargs) in [1, 2]
-
- if len(qargs) == 1:
- specific_fidelity = calibration.ionq_calibration["avg_1Q"]
- elif len(qargs) == 2:
- specific_fidelity = calibration.ionq_calibration["avg_2Q"]
- res *= specific_fidelity
- elif "rigetti_aspen_m2" in device:
- mapping = get_rigetti_qubit_dict()
- for instruction, qargs, _cargs in qc.data:
- gate_type = instruction.name
-
- assert gate_type in ["rx", "rz", "cz", "measure", "barrier"]
- if gate_type != "barrier":
- assert len(qargs) in [1, 2]
- first_qubit = calc_qubit_index(qargs, qc.qregs, 0)
- if len(qargs) == 1:
- if gate_type == "measure":
- specific_fidelity = calibration.rigetti_m2_calibration["fid_1Q_readout"][
- mapping[str(first_qubit)]
- ]
- else:
- specific_fidelity = calibration.rigetti_m2_calibration["fid_1Q"][mapping[str(first_qubit)]]
+ mapping = get_rigetti_qubit_dict()
+ for instruction, qargs, _cargs in qc.data:
+ gate_type = instruction.name
+
+ assert gate_type in ["rx", "rz", "cz", "measure", "barrier"]
+ if gate_type != "barrier":
+ assert len(qargs) in [1, 2]
+ first_qubit = calc_qubit_index(qargs, qc.qregs, 0)
+ if len(qargs) == 1:
+ if gate_type == "measure":
+ specific_fidelity = calibration.rigetti_m2_calibration["fid_1Q_readout"][mapping[str(first_qubit)]]
else:
- second_qubit = calc_qubit_index(qargs, qc.qregs, 1)
- tmp = (
- str(
- min(
- int(mapping[str(first_qubit)]),
- int(mapping[str(second_qubit)]),
- )
+ specific_fidelity = calibration.rigetti_m2_calibration["fid_1Q"][mapping[str(first_qubit)]]
+ else:
+ second_qubit = calc_qubit_index(qargs, qc.qregs, 1)
+ tmp = (
+ str(
+ min(
+ int(mapping[str(first_qubit)]),
+ int(mapping[str(second_qubit)]),
)
- + "-"
- + str(
- max(
- int(mapping[str(first_qubit)]),
- int(mapping[str(second_qubit)]),
- )
+ )
+ + "-"
+ + str(
+ max(
+ int(mapping[str(first_qubit)]),
+ int(mapping[str(second_qubit)]),
)
)
- if (
- calibration.rigetti_m2_calibration["fid_2Q_CZ"].get(tmp) is None
- or calibration.rigetti_m2_calibration["fid_2Q_CZ"][tmp] is None
- ):
- specific_fidelity = calibration.rigetti_m2_calibration["avg_2Q"]
- else:
- specific_fidelity = calibration.rigetti_m2_calibration["fid_2Q_CZ"][tmp]
+ )
+ if (
+ calibration.rigetti_m2_calibration["fid_2Q_CZ"].get(tmp) is None
+ or calibration.rigetti_m2_calibration["fid_2Q_CZ"][tmp] is None
+ ):
+ specific_fidelity = calibration.rigetti_m2_calibration["avg_2Q"]
+ else:
+ specific_fidelity = calibration.rigetti_m2_calibration["fid_2Q_CZ"][tmp]
+
+ res *= specific_fidelity
+ return res
- res *= specific_fidelity
+def calc_expected_fidelity_quantinuum_h2(qc: QuantumCircuit) -> float:
+ res = 1.0
+ calibration = Calibration.Calibration().quantinuum_h2_calibration
+ for instruction, qargs, _cargs in qc.data:
+ gate_type = instruction.name
+ assert gate_type in ["rzz", "rz", "ry", "rx", "measure", "barrier"]
+ if gate_type != "barrier":
+ assert len(qargs) in [1, 2]
+ if len(qargs) == 1:
+ specific_fidelity = calibration["avg_1Q"]
+ elif len(qargs) == 2:
+ specific_fidelity = calibration["avg_2Q"]
+ res *= specific_fidelity
+ return res
+
+
+def calc_expected_fidelity_ionq(qc: QuantumCircuit, device_name: str) -> float:
+ if device_name == "ionq_harmony":
+ calibration_data = Calibration.Calibration().ionq_harmony_calibration
+ elif device_name == "ionq_aria1":
+ calibration_data = Calibration.Calibration().ionq_aria1_calibration
else:
- error_msg = "Device not supported"
- raise ValueError(error_msg)
+ msg = "Device not supported"
+ raise ValueError(msg)
- return cast(float, np.round(res, precision))
+ res = 1.0
+ for instruction, qargs, _cargs in qc.data:
+ gate_type = instruction.name
+
+ assert gate_type in ["rxx", "rz", "ry", "rx", "measure", "barrier"]
+ if gate_type != "barrier":
+ assert len(qargs) in [1, 2]
+ if len(qargs) == 1:
+ specific_fidelity = calibration_data["avg_1Q"]
+ elif len(qargs) == 2:
+ specific_fidelity = calibration_data["avg_2Q"]
+ res *= specific_fidelity
+ return res
+
+
+def calc_expected_fidelity_oqc_lucy(qc: QuantumCircuit) -> float:
+ res = 1.0
+ calibration = Calibration.Calibration().oqc_lucy_calibration
+ for instruction, qargs, _cargs in qc.data:
+ gate_type = instruction.name
+
+ assert gate_type in ["rz", "sx", "x", "ecr", "measure", "barrier"]
+ if gate_type != "barrier":
+ assert len(qargs) in [1, 2]
+ first_qubit = calc_qubit_index(qargs, qc.qregs, 0)
+ if len(qargs) == 1 and gate_type != "measure":
+ specific_fidelity = calibration["fid_1Q"][str(first_qubit)]
+ elif len(qargs) == 1 and gate_type == "measure":
+ specific_fidelity = calibration["fid_1Q_readout"][str(first_qubit)]
+ elif len(qargs) == 2:
+ second_qubit = calc_qubit_index(qargs, qc.qregs, 1)
+ tmp = str(first_qubit) + "-" + str(second_qubit)
+ if calibration["fid_2Q"].get(tmp) is None:
+ specific_fidelity = calibration["avg_2Q"]
+ else:
+ specific_fidelity = calibration["fid_2Q"][tmp]
+
+ res *= specific_fidelity
+ return res
+
+
+def calc_expected_fidelity_ibm(qc: QuantumCircuit, device_name: str) -> float:
+ if device_name == "ibm_montreal":
+ calibration = Calibration.Calibration().ibm_montreal_calibration
+ elif device_name == "ibm_washington":
+ calibration = Calibration.Calibration().ibm_washington_calibration
+ else:
+ msg = "Device not supported"
+ raise ValueError(msg)
+
+ res = 1.0
+ for instruction, qargs, _cargs in qc.data:
+ gate_type = instruction.name
+
+ assert gate_type in ["rz", "sx", "x", "cx", "measure", "barrier"]
+
+ if gate_type != "barrier":
+ assert len(qargs) in [1, 2]
+ first_qubit = calc_qubit_index(qargs, qc.qregs, 0)
+ if len(qargs) == 1:
+ try:
+ if gate_type == "measure":
+ specific_error: float = calibration.readout_error(first_qubit)
+ else:
+ specific_error = calibration.gate_error(gate_type, [first_qubit])
+ except Exception as e:
+ raise RuntimeError(
+ "Error in IBM backend.gate_error(): "
+ + str(e)
+ + ", "
+ + device_name
+ + ", "
+ + first_qubit
+ + ", "
+ + instruction
+ + ", "
+ + qargs
+ ) from None
+ else:
+ second_qubit = calc_qubit_index(qargs, qc.qregs, 1)
+ try:
+ specific_error = calibration.gate_error(gate_type, [first_qubit, second_qubit])
+ if specific_error == 1:
+ logger.warning(
+ "Gate error is 1.0 for: "
+ + device_name
+ + ", "
+ + gate_type
+ + ", "
+ + str(first_qubit)
+ + ", "
+ + str(second_qubit),
+ "mean error is used instead",
+ )
+ if device_name == "ibm_washington":
+ specific_error = calibration.ibm_washington_cx_mean_error
+ elif device_name == "ibm_montreal":
+ specific_error = calibration.ibm_montreal_cx_mean_error
+ except Exception as e:
+ raise RuntimeError(
+ "Error in IBM backend.gate_error(): "
+ + str(e)
+ + ", "
+ + device_name
+ + ", "
+ + first_qubit
+ + ", "
+ + second_qubit
+ + ", "
+ + instruction
+ + ", "
+ + qargs
+ ) from None
+
+ res *= 1 - specific_error
+ return res
diff --git a/src/mqt/predictor/rl/Predictor.py b/src/mqt/predictor/rl/Predictor.py
index 7c2ee588f..883ace488 100644
--- a/src/mqt/predictor/rl/Predictor.py
+++ b/src/mqt/predictor/rl/Predictor.py
@@ -1,116 +1,90 @@
from __future__ import annotations
import logging
-import time
-from pathlib import Path
-from typing import Any, get_args
-
-import numpy as np
-from joblib import Parallel, delayed
-from mqt.predictor import rl
-from pytket import OpType
-from pytket.architecture import Architecture # type: ignore[attr-defined]
-from pytket.extensions.qiskit import qiskit_to_tk, tk_to_qiskit
-from pytket.passes import ( # type: ignore[attr-defined]
- FullPeepholeOptimise,
- PlacementPass,
- RoutingPass,
- auto_rebase_pass,
-)
-from pytket.placement import GraphPlacement # type: ignore[attr-defined]
-from qiskit import QuantumCircuit, transpile
+from typing import TYPE_CHECKING
+
+from mqt.predictor import reward, rl
from sb3_contrib import MaskablePPO
from sb3_contrib.common.maskable.policies import MaskableMultiInputActorCriticPolicy
from sb3_contrib.common.maskable.utils import get_action_masks
-logger = logging.getLogger("mqtpredictor")
+if TYPE_CHECKING:
+ from qiskit import QuantumCircuit
+
+logger = logging.getLogger("mqt-predictor")
PATH_LENGTH = 260
class Predictor:
- def __init__(self, verbose: int = 0):
- if verbose == 1:
- lvl = logging.INFO
- elif verbose == 2:
- lvl = logging.DEBUG
- else:
- lvl = logging.WARNING
- logger.setLevel(lvl)
+ """The Predictor class is used to train a reinforcement learning model for a given figure of merit and device such that it acts as a compiler."""
+
+ def __init__(self, figure_of_merit: reward.figure_of_merit, device_name: str, logger_level: int = logging.INFO):
+ logger.setLevel(logger_level)
+
+ self.model = None
+ self.env = rl.PredictorEnv(figure_of_merit, device_name)
+ self.device_name = device_name
+ self.figure_of_merit = figure_of_merit
+
+ def load_model(self) -> None:
+ self.model = rl.helper.load_model("model_" + self.figure_of_merit + "_" + self.device_name)
def compile_as_predicted(
- self, qc: QuantumCircuit | str, opt_objective: rl.helper.reward_functions = "fidelity"
+ self,
+ qc: QuantumCircuit,
) -> tuple[QuantumCircuit, list[str]]:
- if not isinstance(qc, QuantumCircuit):
- if len(qc) < PATH_LENGTH and Path(qc).exists():
- qc = QuantumCircuit.from_qasm_file(qc)
- elif "OPENQASM" in qc:
- qc = QuantumCircuit.from_qasm_str(qc)
+ """Compiles a given quantum circuit such that the given figure of merit is maximized by using the respectively trained optimized compiler.
+
+ Args:
+ qc (QuantumCircuit: The quantum circuit to be compiled or the path to a qasm file containing the quantum circuit.
+
+ Returns:
+ tuple[QuantumCircuit, list[str]] | bool: Returns a tuple containing the compiled quantum circuit and the compilation information. If compilation fails, False is returned.
+ """
+ if not self.model:
+ try:
+ self.load_model()
+ except Exception as e:
+ msg = "Model cannot be loaded. Try to train a local model using 'Predictor.train_model(<...>)'"
+ raise Exception(msg) from e
- model = rl.helper.load_model("model_" + opt_objective)
- env = rl.PredictorEnv(opt_objective)
- obs, _ = env.reset(qc)
+ assert self.model
+ obs, _ = self.env.reset(qc) # type: ignore[unreachable]
used_compilation_passes = []
terminated = False
truncated = False
while not (terminated or truncated):
- action_masks = get_action_masks(env)
- action, _ = model.predict(obs, action_masks=action_masks)
+ action_masks = get_action_masks(self.env)
+ action, _ = self.model.predict(obs, action_masks=action_masks)
action = int(action)
- action_item = env.action_set[action]
+ action_item = self.env.action_set[action]
used_compilation_passes.append(action_item["name"])
- obs, reward_val, terminated, truncated, info = env.step(action)
- env.state._layout = env.layout
- return env.state, used_compilation_passes
-
- def evaluate_sample_circuit(self, file: str) -> dict[str, Any]:
- logger.info("Evaluate file: " + file)
-
- # reward_functions = ["fidelity", "critical_depth", "gate_ratio", "mix"]
- reward_functions = get_args(rl.helper.reward_functions)
- results = []
- for rew in reward_functions:
- results.append(self.computeRewards(file, "RL", rew))
+ obs, reward_val, terminated, truncated, info = self.env.step(action)
+ self.env.state._layout = self.env.layout
- results.append(self.computeRewards(file, "qiskit_o3"))
- results.append(self.computeRewards(file, "tket"))
+ if not self.env.error_occured:
+ return self.env.state, used_compilation_passes
- combined_res: dict[str, Any] = {
- "benchmark": str(Path(file).stem).replace("_", " ").split(" ")[0],
- "num_qubits": str(Path(file).stem).replace("_", " ").split(" ")[-1],
- }
+ msg = "Error occurred during compilation."
+ raise Exception(msg)
- for res in results:
- combined_res.update(res.get_dict())
- return combined_res
-
- def evaluate_all_sample_circuits(self) -> None:
- res_csv = []
-
- results = Parallel(n_jobs=-1, verbose=3, backend="threading")(
- delayed(self.evaluate_sample_circuit)(str(file))
- for file in list(rl.helper.get_path_training_circuits().glob("*.qasm"))
- )
- res_csv.append(list(results[0].keys()))
- for res in results:
- res_csv.append(list(res.values()))
- np.savetxt(
- rl.helper.get_path_trained_model() / "res.csv",
- res_csv,
- delimiter=",",
- fmt="%s",
- )
-
- def train_all_models(
+ def train_model(
self,
timesteps: int = 1000,
- reward_functions: list[rl.helper.reward_functions] | None = None,
model_name: str = "model",
verbose: int = 2,
test: bool = False,
) -> None:
- if reward_functions is None:
- reward_functions = ["fidelity"]
+ """Trains all models for the given reward functions and device.
+
+ Args:
+ timesteps (int, optional): The number of timesteps to train the model. Defaults to 1000.
+ model_name (str, optional): The name of the model. Defaults to "model".
+ verbose (int, optional): The verbosity level. Defaults to 2.
+ test (bool, optional): Whether to train the model for testing purposes. Defaults to False.
+ """
+
if test:
n_steps = 100
progress_bar = False
@@ -118,80 +92,18 @@ def train_all_models(
n_steps = 2048
progress_bar = True
- for rew in reward_functions:
- logger.debug("Start training for: " + rew)
- env = rl.PredictorEnv(reward_function=rew)
-
- model = MaskablePPO(
- MaskableMultiInputActorCriticPolicy,
- env,
- verbose=verbose,
- tensorboard_log="./" + model_name + "_" + rew,
- gamma=0.98,
- n_steps=n_steps,
- )
- model.learn(total_timesteps=timesteps, progress_bar=progress_bar)
- model.save(rl.helper.get_path_trained_model() / (model_name + "_" + rew))
-
- def computeRewards(
- self,
- benchmark: str,
- used_setup: str,
- reward_function: rl.helper.reward_functions = "fidelity",
- ) -> rl.Result:
- if used_setup == "RL":
- model = rl.helper.load_model("model_" + reward_function)
- env = rl.PredictorEnv(reward_function)
- obs, _ = env.reset(benchmark)
- start_time = time.time()
- terminated = False
- truncated = False
- while not (terminated or truncated):
- action_masks = get_action_masks(env)
- action, _ = model.predict(obs, action_masks=action_masks)
- action = int(action)
- obs, reward_val, terminated, truncated, info = env.step(action)
-
- duration = time.time() - start_time
-
- return rl.Result(
- benchmark,
- used_setup + "_" + reward_function,
- duration,
- env.state,
- env.device,
- )
-
- if used_setup == "qiskit_o3":
- qc = QuantumCircuit.from_qasm_file(benchmark)
- start_time = time.time()
- transpiled_qc_qiskit = transpile(
- qc,
- basis_gates=rl.helper.get_ibm_native_gates(),
- coupling_map=rl.helper.get_cmap_from_devicename("ibm_washington"),
- optimization_level=3,
- seed_transpiler=1,
- )
- duration = time.time() - start_time
-
- return rl.Result(benchmark, used_setup, duration, transpiled_qc_qiskit, "ibm_washington")
-
- if used_setup == "tket":
- qc = QuantumCircuit.from_qasm_file(benchmark)
- tket_qc = qiskit_to_tk(qc)
- arch = Architecture(rl.helper.get_cmap_from_devicename("ibm_washington"))
- ibm_rebase = auto_rebase_pass({OpType.Rz, OpType.SX, OpType.X, OpType.CX, OpType.Measure})
-
- start_time = time.time()
- ibm_rebase.apply(tket_qc)
- FullPeepholeOptimise(target_2qb_gate=OpType.TK2).apply(tket_qc)
- PlacementPass(GraphPlacement(arch)).apply(tket_qc)
- RoutingPass(arch).apply(tket_qc)
- ibm_rebase.apply(tket_qc)
- duration = time.time() - start_time
- transpiled_qc_tket = tk_to_qiskit(tket_qc)
-
- return rl.Result(benchmark, used_setup, duration, transpiled_qc_tket, "ibm_washington")
-
- error_msg = "Unknown setup. Use either 'RL', 'qiskit_o3' or 'tket'."
- raise ValueError(error_msg)
+ logger.debug("Start training for: " + self.figure_of_merit + " on " + self.device_name)
+ env = rl.PredictorEnv(reward_function=self.figure_of_merit, device_name=self.device_name)
+
+ model = MaskablePPO(
+ MaskableMultiInputActorCriticPolicy,
+ env,
+ verbose=verbose,
+ tensorboard_log="./" + model_name + "_" + self.figure_of_merit + "_" + self.device_name,
+ gamma=0.98,
+ n_steps=n_steps,
+ )
+ model.learn(total_timesteps=timesteps, progress_bar=progress_bar)
+ model.save(
+ rl.helper.get_path_trained_model() / (model_name + "_" + self.figure_of_merit + "_" + self.device_name)
+ )
diff --git a/src/mqt/predictor/rl/PredictorEnv.py b/src/mqt/predictor/rl/PredictorEnv.py
index 34d90fb49..741e9b655 100644
--- a/src/mqt/predictor/rl/PredictorEnv.py
+++ b/src/mqt/predictor/rl/PredictorEnv.py
@@ -16,36 +16,32 @@
from qiskit.transpiler.passes import CheckMap, GatesInBasis
from qiskit.transpiler.runningpassmanager import TranspileLayout
-logger = logging.getLogger("mqtpredictor")
+logger = logging.getLogger("mqt-predictor")
class PredictorEnv(Env): # type: ignore[misc]
- def __init__(self, reward_function: rl.helper.reward_functions = "fidelity"):
+ """Predictor environment for reinforcement learning."""
+
+ def __init__(
+ self, reward_function: reward.figure_of_merit = "expected_fidelity", device_name: str = "ibm_washington"
+ ):
logger.info("Init env: " + reward_function)
self.action_set = {}
- self.actions_platform = []
self.actions_synthesis_indices = []
- self.actions_devices_indices = []
self.actions_layout_indices = []
self.actions_routing_indices = []
+ self.actions_mapping_indices = []
self.actions_opt_indices = []
+ self.used_actions: list[str] = []
+ self.device = rl.helper.get_device(device_name)
index = 0
- for elem in rl.helper.get_actions_platform_selection():
- self.action_set[index] = elem
- self.actions_platform.append(index)
- index += 1
for elem in rl.helper.get_actions_synthesis():
self.action_set[index] = elem
self.actions_synthesis_indices.append(index)
index += 1
-
- for elem in rl.helper.get_actions_devices():
- self.action_set[index] = elem
- self.actions_devices_indices.append(index)
- index += 1
for elem in rl.helper.get_actions_layout():
self.action_set[index] = elem
self.actions_layout_indices.append(index)
@@ -54,11 +50,14 @@ def __init__(self, reward_function: rl.helper.reward_functions = "fidelity"):
self.action_set[index] = elem
self.actions_routing_indices.append(index)
index += 1
-
for elem in rl.helper.get_actions_opt():
self.action_set[index] = elem
self.actions_opt_indices.append(index)
index += 1
+ for elem in rl.helper.get_actions_mapping():
+ self.action_set[index] = elem
+ self.actions_mapping_indices.append(index)
+ index += 1
self.action_set[index] = rl.helper.get_action_terminate()
self.action_terminate_index = index
@@ -66,10 +65,11 @@ def __init__(self, reward_function: rl.helper.reward_functions = "fidelity"):
self.reward_function = reward_function
self.action_space = Discrete(len(self.action_set.keys()))
self.num_steps = 0
+ self.layout = None
spaces = {
"num_qubits": Discrete(128),
- "depth": Discrete(100000),
+ "depth": Discrete(1000000),
"program_communication": Box(low=0, high=1, shape=(1,), dtype=np.float32),
"critical_depth": Box(low=0, high=1, shape=(1,), dtype=np.float32),
"entanglement_ratio": Box(low=0, high=1, shape=(1,), dtype=np.float32),
@@ -77,13 +77,11 @@ def __init__(self, reward_function: rl.helper.reward_functions = "fidelity"):
"liveness": Box(low=0, high=1, shape=(1,), dtype=np.float32),
}
self.observation_space = Dict(spaces)
- self.native_gateset_name = ""
- self.native_gates = None
- self.device = ""
- self.cmap = None
- self.layout = None
+ self.filename = ""
def step(self, action: int) -> tuple[dict[str, Any], float, bool, bool, dict[Any, Any]]:
+ """Executes the given action and returns the new state, the reward, whether the episode is done, whether the episode is truncated and additional information."""
+ self.used_actions.append(str(self.action_set[action].get("name")))
altered_qc = self.apply_action(action)
if not altered_qc:
return (
@@ -99,7 +97,8 @@ def step(self, action: int) -> tuple[dict[str, Any], float, bool, bool, dict[Any
self.valid_actions = self.determine_valid_actions_for_state()
if len(self.valid_actions) == 0:
- return rl.helper.create_feature_dict(self.state), 0, True, False, {}
+ msg = "No valid actions left."
+ raise RuntimeError(msg)
if action == self.action_terminate_index:
reward_val = self.calculate_reward()
@@ -116,18 +115,16 @@ def step(self, action: int) -> tuple[dict[str, Any], float, bool, bool, dict[Any
return obs, reward_val, done, False, {}
def calculate_reward(self) -> Any:
- if self.reward_function == "fidelity":
- return reward.expected_fidelity(self.state, self.device)
+ """Calculates and returns the reward for the current state."""
+ if self.reward_function == "expected_fidelity":
+ return reward.expected_fidelity(self.state, self.device["name"])
if self.reward_function == "critical_depth":
return reward.crit_depth(self.state)
- if self.reward_function == "mix":
- return reward.mix(self.state, self.device)
- if self.reward_function == "gate_ratio":
- return reward.gate_ratio(self.state)
error_msg = f"Reward function {self.reward_function} not supported." # type: ignore[unreachable]
raise ValueError(error_msg)
def render(self) -> None:
+ """Renders the current state."""
print(self.state.draw())
def reset(
@@ -136,70 +133,75 @@ def reset(
seed: int | None = None,
options: dict[str, Any] | None = None, # noqa: ARG002
) -> tuple[QuantumCircuit, dict[str, Any]]:
+ """Resets the environment to the given state or a random state.
+
+ Args:
+ qc (Path | str | QuantumCircuit | None, optional): The quantum circuit to be compiled or the path to a qasm file containing the quantum circuit. Defaults to None.
+ seed (int | None, optional): The seed to be used for the random number generator. Defaults to None.
+ options (dict[str, Any] | None, optional): Additional options. Defaults to None.
+
+ Returns:
+ tuple[QuantumCircuit, dict[str, Any]]: The initial state and additional information.
+ """
super().reset(seed=seed)
if isinstance(qc, QuantumCircuit):
self.state = qc
elif qc:
self.state = QuantumCircuit.from_qasm_file(str(qc))
else:
- self.state = rl.helper.get_state_sample()
+ self.state, self.filename = rl.helper.get_state_sample(self.device["max_qubits"])
self.action_space = Discrete(len(self.action_set.keys()))
self.num_steps = 0
+ self.used_actions = []
- self.native_gateset_name = ""
- self.native_gates = None
- self.device = ""
- self.cmap = None
self.layout = None
- self.valid_actions = self.get_platform_valid_actions_for_state()
+ self.valid_actions = self.actions_opt_indices + self.actions_synthesis_indices
+
+ self.error_occured = False
return rl.helper.create_feature_dict(self.state), {}
def action_masks(self) -> list[bool]:
+ """Returns a list of valid actions for the current state."""
return [action in self.valid_actions for action in self.action_set]
- def apply_action(self, action_index: int) -> QuantumCircuit:
- if action_index in self.actions_platform:
- self.native_gateset_name = self.action_set[action_index]["name"]
- self.native_gates = self.action_set[action_index]["gates"]
- elif action_index in self.actions_devices_indices:
- self.device = self.action_set[action_index]["name"]
- self.cmap = self.action_set[action_index]["cmap"]
-
+ def apply_action(self, action_index: int) -> QuantumCircuit | None:
+ """Applies the given action to the current state and returns the altered state."""
if action_index in self.action_set:
action = self.action_set[action_index]
if action["name"] == "terminate":
return self.state
-
- if action_index in self.actions_platform:
- self.native_gates = self.action_set[action_index]["gates"]
- return self.state
-
- if action_index in self.actions_devices_indices:
- self.cmap = self.action_set[action_index]["cmap"]
- return self.state
-
- if action_index in self.actions_layout_indices + self.actions_routing_indices:
- transpile_pass = action["transpile_pass"](self.cmap)
+ if (
+ action_index
+ in self.actions_layout_indices + self.actions_routing_indices + self.actions_mapping_indices
+ ):
+ transpile_pass = action["transpile_pass"](self.device["cmap"])
elif action_index in self.actions_synthesis_indices:
- transpile_pass = action["transpile_pass"](self.native_gates)
+ transpile_pass = action["transpile_pass"](self.device["native_gates"])
else:
transpile_pass = action["transpile_pass"]
if action["origin"] == "qiskit":
- pm = PassManager(transpile_pass)
try:
+ if action["name"] == "QiskitO3":
+ pm = PassManager()
+ pm.append(
+ action["transpile_pass"](self.device["native_gates"], CouplingMap(self.device["cmap"])),
+ do_while=action["do_while"],
+ )
+ else:
+ pm = PassManager(transpile_pass)
altered_qc = pm.run(self.state)
except Exception as e:
- raise RuntimeError(
- "Error in executing Qiskit transpile pass: "
- + action["name"]
- + ", "
- + self.state.name
- + ", "
- + str(e)
- ) from None
- if action_index in self.actions_layout_indices:
+ logger.error(
+ "Error in executing Qiskit transpile pass for {action} at step {i} for {filename}: {e}".format(
+ action=action["name"], i=self.num_steps, filename=self.filename, e=e
+ )
+ )
+
+ self.error_occured = True
+ return None
+ if action_index in self.actions_layout_indices + self.actions_mapping_indices:
assert pm.property_set["layout"]
self.layout = TranspileLayout(
initial_layout=pm.property_set["layout"],
@@ -214,14 +216,14 @@ def apply_action(self, action_index: int) -> QuantumCircuit:
elem.apply(tket_qc)
altered_qc = tk_to_qiskit(tket_qc)
except Exception as e:
- raise RuntimeError(
- "Error in executing TKET transpile pass: "
- + action["name"]
- + ", "
- + self.state.name
- + ", "
- + str(e),
- ) from None
+ logger.error(
+ "Error in executing TKET transpile pass for {action} at step {i} for {filename}: {e}".format(
+ action=action["name"], i=self.num_steps, filename=self.filename, e=e
+ )
+ )
+ self.error_occured = True
+ return None
+
else:
error_msg = f"Origin {action['origin']} not supported."
raise ValueError(error_msg)
@@ -233,48 +235,23 @@ def apply_action(self, action_index: int) -> QuantumCircuit:
return altered_qc
def determine_valid_actions_for_state(self) -> list[int]:
- if self.native_gates is None:
- return self.get_platform_valid_actions_for_state() + self.actions_opt_indices
-
- if not self.device: # type: ignore[unreachable]
- return self.get_device_action_indices_for_nat_gates()
-
- check_nat_gates = GatesInBasis(basis_gates=self.native_gates)
+ """Determines and returns the valid actions for the current state."""
+ check_nat_gates = GatesInBasis(basis_gates=self.device["native_gates"])
check_nat_gates(self.state)
only_nat_gates = check_nat_gates.property_set["all_gates_in_basis"]
if not only_nat_gates:
return self.actions_synthesis_indices + self.actions_opt_indices
- check_mapping = CheckMap(coupling_map=CouplingMap(self.cmap))
+ check_mapping = CheckMap(coupling_map=CouplingMap(self.device["cmap"]))
check_mapping(self.state)
mapped = check_mapping.property_set["is_swap_mapped"]
if mapped and self.layout is not None:
- return [self.action_terminate_index, *self.actions_opt_indices]
+ return [self.action_terminate_index, *self.actions_opt_indices] # type: ignore[unreachable]
- # No layout applied yet
if self.state._layout is not None:
return self.actions_routing_indices
- return self.actions_layout_indices + self.actions_opt_indices
-
- def get_device_action_indices_for_nat_gates(self) -> list[int]:
- nat_gate_index = -1
- for key in self.action_set:
- if self.action_set[key]["name"] == self.native_gateset_name:
- nat_gate_index = key
- break
- potential_devices_names = self.action_set[nat_gate_index]["devices"]
- potential_devices_indices = []
- for dev in potential_devices_names:
- for key in self.action_set:
- if self.action_set[key]["name"] == dev and self.state.num_qubits <= self.action_set[key]["max_qubits"]:
- potential_devices_indices.append(key)
- return potential_devices_indices
-
- def get_platform_valid_actions_for_state(self) -> list[int]:
- valid_platform_indices = []
- for platform_action in self.actions_platform:
- if self.state.num_qubits <= self.action_set[platform_action]["max_qubit_size"]:
- valid_platform_indices.append(platform_action)
- return valid_platform_indices
+
+ # No layout applied yet
+ return self.actions_mapping_indices + self.actions_layout_indices + self.actions_opt_indices
diff --git a/src/mqt/predictor/rl/Result.py b/src/mqt/predictor/rl/Result.py
deleted file mode 100644
index 8b30ebe4f..000000000
--- a/src/mqt/predictor/rl/Result.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-from mqt.predictor import reward
-
-if TYPE_CHECKING:
- from qiskit import QuantumCircuit
-
-
-class Result:
- """
- Class to calculate and store the result of a compiler for a given benchmark.
-
- Attributes
- benchmark: str - name of the benchmark
- used_setup: str - name of the used setup
- time: float - time needed to compile the circuit
- fidelity: float - fidelity reward of the compiled circuit
- depth: float - depth reward of the compiled circuit
- gate_ratio: float - gate ratio reward of the compiled circuit
- mix: float - mix reward of the compiled circuit
-
- """
-
- def __init__(
- self,
- benchmark: str,
- used_setup: str,
- duration: float,
- qc: QuantumCircuit,
- device: str,
- ) -> None:
- rew_fid = reward.expected_fidelity(qc, device)
- rew_crit_depth = reward.crit_depth(qc)
- rew_gate_ratio = reward.gate_ratio(qc)
- rew_mix = reward.mix(qc, device)
-
- self.benchmark: str = benchmark
- self.used_setup: str = used_setup
- self.time: float = duration
- self.fidelity: float = rew_fid
- self.critical_depth: float = rew_crit_depth
- self.gate_ratio: float = rew_gate_ratio
- self.mix: float = rew_mix
-
- def get_dict(self) -> dict[str, float]:
- return {
- self.used_setup + "_" + "time": self.time,
- self.used_setup + "_" + "fidelity": self.fidelity,
- self.used_setup + "_" + "critical_depth": self.critical_depth,
- self.used_setup + "_" + "gate_ratio": self.gate_ratio,
- self.used_setup + "_" + "mix": self.mix,
- }
diff --git a/src/mqt/predictor/rl/__init__.py b/src/mqt/predictor/rl/__init__.py
index 173ff2109..621a12930 100644
--- a/src/mqt/predictor/rl/__init__.py
+++ b/src/mqt/predictor/rl/__init__.py
@@ -1,13 +1,9 @@
-from mqt.predictor.rl import helper
from mqt.predictor.rl.helper import qcompile
from mqt.predictor.rl.Predictor import Predictor
from mqt.predictor.rl.PredictorEnv import PredictorEnv
-from mqt.predictor.rl.Result import Result
__all__ = [
- "helper",
"qcompile",
"Predictor",
"PredictorEnv",
- "Result",
]
diff --git a/src/mqt/predictor/rl/helper.py b/src/mqt/predictor/rl/helper.py
index ee68dd037..7f1d32d9c 100644
--- a/src/mqt/predictor/rl/helper.py
+++ b/src/mqt/predictor/rl/helper.py
@@ -3,27 +3,27 @@
import logging
import sys
from pathlib import Path
-from typing import TYPE_CHECKING, Any, Literal
+from typing import TYPE_CHECKING, Any
import numpy as np
import requests
-from mqt.bench.utils import calc_supermarq_features
-from mqt.predictor import rl
+from mqt.bench.qiskit_helper import get_native_gates
+from mqt.bench.utils import calc_supermarq_features, get_cmap_from_devicename
+from mqt.predictor import reward, rl
from packaging import version
-from pytket.architecture import Architecture # type: ignore[attr-defined]
-from pytket.circuit import Circuit, Node, OpType, Qubit # type: ignore[attr-defined]
-from pytket.passes import ( # type: ignore[attr-defined]
+from pytket.architecture import Architecture
+from pytket.circuit import Circuit, Node, Qubit
+from pytket.passes import (
CliffordSimp,
FullPeepholeOptimise,
PeepholeOptimise2Q,
RemoveRedundancies,
RoutingPass,
)
-from pytket.placement import place_with_map # type: ignore[attr-defined]
+from pytket.placement import place_with_map
from qiskit import QuantumCircuit
from qiskit.circuit.equivalence_library import StandardEquivalenceLibrary
from qiskit.circuit.library import XGate, ZGate
-from qiskit.providers.fake_provider import FakeMontreal, FakeWashington
from qiskit.transpiler import CouplingMap
from qiskit.transpiler.passes import (
ApplyLayout,
@@ -35,44 +35,78 @@
ConsolidateBlocks,
CXCancellation,
DenseLayout,
+ Depth,
EnlargeWithAncilla,
+ FixedPoint,
FullAncillaAllocation,
+ GatesInBasis,
InverseCancellation,
+ MinimumPoint,
Optimize1qGatesDecomposition,
OptimizeCliffords,
RemoveDiagonalGatesBeforeMeasure,
SabreLayout,
SabreSwap,
+ Size,
StochasticSwap,
TrivialLayout,
+ UnitarySynthesis,
)
+from qiskit.transpiler.preset_passmanagers import common
+from qiskit.transpiler.runningpassmanager import ConditionalController
from sb3_contrib import MaskablePPO
from tqdm import tqdm
+if TYPE_CHECKING:
+ from numpy.typing import NDArray
+
if TYPE_CHECKING or sys.version_info >= (3, 10, 0):
from importlib import metadata, resources
else:
import importlib_metadata as metadata
import importlib_resources as resources
-reward_functions = Literal["fidelity", "critical_depth", "mix", "gate_ratio"]
+logger = logging.getLogger("mqt-predictor")
+
+
+NUM_ACTIONS_OPT = 13
+NUM_ACTIONS_LAYOUT = 3
+NUM_ACTIONS_ROUTING = 4
+NUM_ACTIONS_SYNTHESIS = 1
+NUM_ACTIONS_TERMINATE = 1
+NUM_ACTIONS_DEVICES = 7
+NUM_ACTIONS_MAPPING = 1
+NUM_FEATURE_VECTOR_ELEMENTS = 7
-logger = logging.getLogger("mqtpredictor")
+def qcompile(
+ qc: QuantumCircuit | str,
+ figure_of_merit: reward.figure_of_merit = "expected_fidelity",
+ device_name: str = "ibm_washington",
+ predictor_singleton: rl.Predictor | None = None,
+) -> tuple[QuantumCircuit, list[str]]:
+ """Compiles a given quantum circuit to a device optimizing for the given figure of merit.
-def qcompile(qc: QuantumCircuit | str, opt_objective: reward_functions = "fidelity") -> QuantumCircuit:
- """Returns the compiled quantum circuit which is compiled following an objective function.
- Keyword arguments:
- qc -- to be compiled quantum circuit or path to a qasm file
- opt_objective -- objective function used for the compilation
- Returns: compiled quantum circuit as Qiskit QuantumCircuit object
+ Args:
+ qc (QuantumCircuit | str): The quantum circuit to be compiled. If a string is given, it is assumed to be a path to a qasm file.
+ figure_of_merit (reward.reward_functions, optional): The figure of merit to be used for compilation. Defaults to "expected_fidelity".
+ device_name (str, optional): The name of the device to compile to. Defaults to "ibm_washington".
+ predictor_singleton (rl.Predictor, optional): A predictor object that is used for compilation. If None, a new predictor object is created. Defaults to None.
+
+ Returns:
+ tuple[QuantumCircuit, list[str]] | bool: Returns a tuple containing the compiled quantum circuit and the compilation information. If compilation fails, False is returned.
"""
- predictor = rl.Predictor()
- return predictor.compile_as_predicted(qc, opt_objective=opt_objective)
+ if predictor_singleton is None:
+ predictor = rl.Predictor(figure_of_merit=figure_of_merit, device_name=device_name)
+ else:
+ predictor = predictor_singleton
+
+ return predictor.compile_as_predicted(qc)
def get_actions_opt() -> list[dict[str, Any]]:
+ """Returns a list of dictionaries containing information about the optimization passes that are available."""
return [
{
"name": "Optimize1qGatesDecomposition",
@@ -126,7 +160,7 @@ def get_actions_opt() -> list[dict[str, Any]]:
},
{
"name": "FullPeepholeOptimiseCX",
- "transpile_pass": [FullPeepholeOptimise(target_2qb_gate=OpType.TK2)],
+ "transpile_pass": [FullPeepholeOptimise()],
"origin": "tket",
},
{
@@ -134,10 +168,39 @@ def get_actions_opt() -> list[dict[str, Any]]:
"transpile_pass": [RemoveRedundancies()],
"origin": "tket",
},
+ {
+ "name": "QiskitO3",
+ "transpile_pass": lambda bgates, cmap: [
+ Collect2qBlocks(),
+ ConsolidateBlocks(basis_gates=bgates),
+ UnitarySynthesis(basis_gates=bgates, coupling_map=cmap),
+ Optimize1qGatesDecomposition(basis=bgates),
+ CommutativeCancellation(basis_gates=bgates),
+ GatesInBasis(bgates),
+ ConditionalController(
+ [
+ pass_
+ for x in common.generate_translation_passmanager(
+ target=None, basis_gates=bgates, coupling_map=cmap
+ ).passes()
+ for pass_ in x["passes"]
+ ],
+ condition=lambda property_set: not property_set["all_gates_in_basis"],
+ ),
+ Depth(recurse=True),
+ FixedPoint("depth"),
+ Size(recurse=True),
+ FixedPoint("size"),
+ MinimumPoint(["depth", "size"], "optimization_loop"),
+ ],
+ "origin": "qiskit",
+ "do_while": lambda property_set: (not property_set["optimization_loop_minimum_point"]),
+ },
]
def get_actions_layout() -> list[dict[str, Any]]:
+ """Returns a list of dictionaries containing information about the layout passes that are available."""
return [
{
"name": "TrivialLayout",
@@ -173,6 +236,7 @@ def get_actions_layout() -> list[dict[str, Any]]:
def get_actions_routing() -> list[dict[str, Any]]:
+ """Returns a list of dictionaries containing information about the routing passes that are available."""
return [
{
"name": "BasicSwap",
@@ -200,36 +264,21 @@ def get_actions_routing() -> list[dict[str, Any]]:
]
-def get_actions_platform_selection() -> list[dict[str, Any]]:
+def get_actions_mapping() -> list[dict[str, Any]]:
+ """Returns a list of dictionaries containing information about the mapping passes that are available."""
return [
{
- "name": "IBM",
- "gates": get_ibm_native_gates(),
- "devices": ["ibm_washington", "ibm_montreal"],
- "max_qubit_size": 127,
- },
- {
- "name": "Rigetti",
- "gates": get_rigetti_native_gates(),
- "devices": ["rigetti_aspen_m2"],
- "max_qubit_size": 80,
- },
- {
- "name": "OQC",
- "gates": get_oqc_native_gates(),
- "devices": ["oqc_lucy"],
- "max_qubit_size": 8,
- },
- {
- "name": "IonQ",
- "gates": get_ionq_native_gates(),
- "devices": ["ionq11"],
- "max_qubit_size": 11,
+ "name": "SabreMapping",
+ "transpile_pass": lambda c: [
+ SabreLayout(coupling_map=CouplingMap(c), skip_routing=False),
+ ],
+ "origin": "qiskit",
},
]
def get_actions_synthesis() -> list[dict[str, Any]]:
+ """Returns a list of dictionaries containing information about the synthesis passes that are available."""
return [
{
"name": "BasisTranslator",
@@ -240,57 +289,70 @@ def get_actions_synthesis() -> list[dict[str, Any]]:
def get_action_terminate() -> dict[str, Any]:
+ """Returns a dictionary containing information about the terminate pass that is available."""
return {"name": "terminate"}
-def get_actions_devices() -> list[dict[str, Any]]:
+def get_devices() -> list[dict[str, Any]]:
+ """Returns a list of dictionaries containing information about the devices that are available."""
return [
{
"name": "ibm_washington",
- "transpile_pass": [],
- "full_connectivity": False,
"cmap": get_cmap_from_devicename("ibm_washington"),
+ "native_gates": get_native_gates("ibm"),
"max_qubits": 127,
},
{
"name": "ibm_montreal",
- "transpile_pass": [],
- "device": "ibm_montreal",
- "full_connectivity": False,
"cmap": get_cmap_from_devicename("ibm_montreal"),
+ "native_gates": get_native_gates("ibm"),
"max_qubits": 27,
},
{
"name": "oqc_lucy",
- "transpile_pass": [],
- "device": "oqc_lucy",
- "full_connectivity": False,
"cmap": get_cmap_from_devicename("oqc_lucy"),
+ "native_gates": get_native_gates("oqc"),
"max_qubits": 8,
},
{
"name": "rigetti_aspen_m2",
- "transpile_pass": [],
- "device": "rigetti_aspen_m2",
- "full_connectivity": False,
"cmap": get_cmap_from_devicename("rigetti_aspen_m2"),
+ "native_gates": get_native_gates("rigetti"),
"max_qubits": 80,
},
{
- "name": "ionq11",
- "transpile_pass": [],
- "device": "ionq11",
- "full_connectivity": True,
- "cmap": get_cmap_from_devicename("ionq11"),
+ "name": "ionq_harmony",
+ "cmap": get_cmap_from_devicename("ionq_harmony"),
+ "native_gates": get_native_gates("ionq"),
"max_qubits": 11,
},
+ {
+ "name": "ionq_aria1",
+ "cmap": get_cmap_from_devicename("ionq_aria1"),
+ "native_gates": get_native_gates("ionq"),
+ "max_qubits": 25,
+ },
+ {
+ "name": "quantinuum_h2",
+ "cmap": get_cmap_from_devicename("quantinuum_h2"),
+ "native_gates": get_native_gates("quantinuum"),
+ "max_qubits": 32,
+ },
]
-def get_state_sample() -> QuantumCircuit:
+def get_state_sample(max_qubits: int | None = None) -> tuple[QuantumCircuit, str]:
+ """Returns a random quantum circuit from the training circuits folder.
+
+ Args:
+ max_qubits (int, None): The maximum number of qubits the returned quantum circuit may have. If no limit is set, it defaults to None.
+
+ Returns:
+ tuple[QuantumCircuit, str]: A tuple containing the random quantum circuit and the path to the file from which it was read.
+ """
file_list = list(get_path_training_circuits().glob("*.qasm"))
- path_zip = get_path_training_circuits() / "mqtbench_sample_circuits.zip"
+ path_zip = get_path_training_circuits() / "training_data_compilation.zip"
if len(file_list) == 0 and path_zip.exists():
import zipfile
@@ -300,99 +362,32 @@ def get_state_sample() -> QuantumCircuit:
file_list = list(get_path_training_circuits().glob("*.qasm"))
assert len(file_list) > 0
- random_index = np.random.randint(len(file_list))
+ found_suitable_qc = False
+ while not found_suitable_qc:
+ random_index = np.random.randint(len(file_list))
+ num_qubits = int(str(file_list[random_index]).split("_")[-1].split(".")[0])
+ if max_qubits and num_qubits > max_qubits:
+ continue
+ found_suitable_qc = True
+
try:
qc = QuantumCircuit.from_qasm_file(str(file_list[random_index]))
except Exception:
raise RuntimeError("Could not read QuantumCircuit from: " + str(file_list[random_index])) from None
- return qc
-
-
-def get_ibm_native_gates() -> list[str]:
- return ["rz", "sx", "x", "cx", "measure"]
-
-
-def get_rigetti_native_gates() -> list[str]:
- return ["rx", "rz", "cz", "measure"]
-
-
-def get_ionq_native_gates() -> list[str]:
- return ["rxx", "rz", "ry", "rx", "measure"]
-
+ return qc, str(file_list[random_index])
-def get_oqc_native_gates() -> list[str]:
- return ["rz", "sx", "x", "ecr", "measure"]
+def create_feature_dict(qc: QuantumCircuit) -> dict[str, int | NDArray[np.float_]]:
+ """Creates a feature dictionary for a given quantum circuit.
-def get_rigetti_aspen_m2_map() -> list[list[int]]:
- """Returns a coupling map of Rigetti Aspen M2 chip."""
- c_map_rigetti = []
- for j in range(5):
- for i in range(7):
- c_map_rigetti.append([i + j * 8, i + 1 + j * 8])
-
- if i == 6:
- c_map_rigetti.append([0 + j * 8, 7 + j * 8])
-
- if j != 0:
- c_map_rigetti.append([j * 8 - 6, j * 8 + 5])
- c_map_rigetti.append([j * 8 - 7, j * 8 + 6])
-
- for j in range(5):
- m = 8 * j + 5 * 8
- for i in range(7):
- c_map_rigetti.append([i + m, i + 1 + m])
-
- if i == 6:
- c_map_rigetti.append([0 + m, 7 + m])
-
- if j != 0:
- c_map_rigetti.append([m - 6, m + 5])
- c_map_rigetti.append([m - 7, m + 6])
-
- for n in range(5):
- c_map_rigetti.append([n * 8 + 3, n * 8 + 5 * 8])
- c_map_rigetti.append([n * 8 + 4, n * 8 + 7 + 5 * 8])
-
- inverted = [[item[1], item[0]] for item in c_map_rigetti]
-
- return c_map_rigetti + inverted
-
-
-def get_ionq11_c_map() -> list[list[int]]:
- ionq11_c_map = []
- for i in range(11):
- for j in range(11):
- if i != j:
- ionq11_c_map.append([i, j])
- return ionq11_c_map
-
-
-def get_cmap_oqc_lucy() -> list[list[int]]:
- """Returns the coupling map of the OQC Lucy quantum computer."""
- # source: https://github.com/aws/amazon-braket-examples/blob/main/examples/braket_features/Verbatim_Compilation.ipynb
-
- # Connections are NOT bidirectional, this is not an accident
- return [[0, 1], [0, 7], [1, 2], [2, 3], [7, 6], [6, 5], [4, 3], [4, 5]]
-
-
-def get_cmap_from_devicename(device: str) -> Any:
- if device == "ibm_washington":
- return FakeWashington().configuration().coupling_map
- if device == "ibm_montreal":
- return FakeMontreal().configuration().coupling_map
- if device == "rigetti_aspen_m2":
- return get_rigetti_aspen_m2_map()
- if device == "oqc_lucy":
- return get_cmap_oqc_lucy()
- if device == "ionq11":
- return get_ionq11_c_map()
- error_msg = "Unknown device name"
- raise ValueError(error_msg)
+ Args:
+ qc (QuantumCircuit): The quantum circuit for which the feature dictionary is created.
+ Returns:
+ dict[str, Any]: The feature dictionary for the given quantum circuit.
+ """
-def create_feature_dict(qc: QuantumCircuit) -> dict[str, Any]:
feature_dict = {
"num_qubits": qc.num_qubits,
"depth": qc.depth(),
@@ -410,18 +405,29 @@ def create_feature_dict(qc: QuantumCircuit) -> dict[str, Any]:
def get_path_training_data() -> Path:
+ """Returns the path to the training data folder used for RL training."""
return Path(str(resources.files("mqt.predictor"))) / "rl" / "training_data"
def get_path_trained_model() -> Path:
+ """Returns the path to the trained model folder used for RL training."""
return get_path_training_data() / "trained_model"
def get_path_training_circuits() -> Path:
+ """Returns the path to the training circuits folder used for RL training."""
return get_path_training_data() / "training_circuits"
def load_model(model_name: str) -> MaskablePPO:
+ """Loads a trained model from the trained model folder.
+
+ Args:
+ model_name (str): The name of the model to be loaded.
+
+ Returns:
+ MaskablePPO: The loaded model.
+ """
path = get_path_trained_model()
if Path(path / (model_name + ".zip")).exists():
@@ -436,13 +442,16 @@ def load_model(model_name: str) -> MaskablePPO:
raise RuntimeError(error_msg) from None
version_found = False
- response = requests.get("https://api.github.com/repos/cda-tum/mqtpredictor/tags")
+ response = requests.get("https://api.github.com/repos/cda-tum/mqt-predictor/tags")
available_versions = []
+ if not response:
+ error_msg = "Querying the GitHub API failed. One reasons could be that the limit of 60 API calls per hour and IP address is exceeded."
+ raise RuntimeError(error_msg)
for elem in response.json():
available_versions.append(elem["name"])
for possible_version in available_versions:
if version.parse(mqtpredictor_module_version) >= version.parse(possible_version):
- url = "https://api.github.com/repos/cda-tum/mqtpredictor/releases/tags/" + possible_version
+ url = "https://api.github.com/repos/cda-tum/mqt-predictor/releases/tags/" + possible_version
response = requests.get(url)
if not response:
error_msg = "Suitable trained models cannot be downloaded since the GitHub API failed. One reasons could be that the limit of 60 API calls per hour and IP address is exceeded."
@@ -468,13 +477,19 @@ def load_model(model_name: str) -> MaskablePPO:
break
if not version_found:
- error_msg = "No suitable model found on GitHub. Please update your mqt.predictort package using 'pip install -U mqt.predictor'."
+ error_msg = "No suitable model found on GitHub. Please update your mqt.predictor package using 'pip install -U mqt.predictor'."
raise RuntimeError(error_msg) from None
return MaskablePPO.load(path / model_name)
def handle_downloading_model(download_url: str, model_name: str) -> None:
+ """Downloads a trained model from the given URL and saves it to the trained model folder.
+
+ Args:
+ download_url (str): The URL from which the model is downloaded.
+ model_name (str): The name of the model to be downloaded.
+ """
logger.info("Start downloading model...")
r = requests.get(download_url)
@@ -502,6 +517,43 @@ class PreProcessTKETRoutingAfterQiskitLayout:
The trivial layout indices that this layout of the physical qubits is the identity mapping.
"""
- def apply(self, circuit: Circuit) -> Circuit:
+ def apply(self, circuit: Circuit) -> None:
+ """Applies the pre-processing step to route a circuit with tket after a Qiskit Layout pass has been applied."""
mapping = {Qubit(i): Node(i) for i in range(circuit.n_qubits)}
place_with_map(circuit=circuit, qmap=mapping)
+
+
+def get_device(device_name: str) -> dict[str, Any]:
+ """Returns the device with the given name.
+
+ Args:
+ device_name (str): The name of the device to be returned.
+
+ Returns:
+ dict[str, Any]: The device with the given name.
+ """
+ devices = get_devices()
+ for device in devices:
+ if device["name"] == device_name:
+ return device
+
+ msg = "No suitable device found."
+ raise RuntimeError(msg)
+
+
+def get_device_index_of_device(device_name: str) -> int:
+ """Returns the index of the device with the given name.
+
+ Args:
+ device_name (str): The name of the device to be returned.
+
+ Returns:
+ int: The index of the device with the given name.
+ """
+ devices = get_devices()
+ for i, device in enumerate(devices):
+ if device["name"] == device_name:
+ return i
+
+ msg = "No suitable device found."
+ raise RuntimeError(msg)
diff --git a/src/mqt/predictor/rl/training_data/trained_model/.gitkeep b/src/mqt/predictor/rl/training_data/trained_model/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/src/mqt/predictor/rl/training_data/trained_model/res.csv b/src/mqt/predictor/rl/training_data/trained_model/res.csv
deleted file mode 100644
index ef262d2f4..000000000
--- a/src/mqt/predictor/rl/training_data/trained_model/res.csv
+++ /dev/null
@@ -1,503 +0,0 @@
-benchmark,num_qubits,RL_fidelity_time,RL_fidelity_fidelity,RL_fidelity_critical_depth,RL_fidelity_gate_ratio,RL_fidelity_mix,RL_critical_depth_time,RL_critical_depth_fidelity,RL_critical_depth_critical_depth,RL_critical_depth_gate_ratio,RL_critical_depth_mix,RL_mix_time,RL_mix_fidelity,RL_mix_critical_depth,RL_mix_gate_ratio,RL_mix_mix,RL_gate_ratio_time,RL_gate_ratio_fidelity,RL_gate_ratio_critical_depth,RL_gate_ratio_gate_ratio,RL_gate_ratio_mix,qiskit_o3_time,qiskit_o3_fidelity,qiskit_o3_critical_depth,qiskit_o3_gate_ratio,qiskit_o3_mix,tket_time,tket_fidelity,tket_critical_depth,tket_gate_ratio,tket_mix
-qft,8,7.4577391147613525,0.1392759269,0.2534246575,0.7260787992,0.1963502922,1.9455680847167969,0.0310686159,0.5735294118,0.8482142857,0.30229901385,2.544236898422241,0.0310686159,0.5735294118,0.8482142857,0.30229901385,3.0637989044189453,0.0310686159,0.5735294118,0.8482142857,0.30229901385,0.2254619598388672,0.1208629318,0.3409090909,0.4782608696,0.23088601135,1.49458909034729,0.2452830875,0.375,0.7793103448,0.31014154375
-portfolioqaoa,7,5.299858808517456,0.0660449134,0.3445692884,0.633744856,0.2053071009,3.966435194015503,0.0224184387,0.6031746032,0.9010989011,0.31279652095000005,3.2549638748168945,0.0224184387,0.6031746032,0.9010989011,0.31279652095000005,4.5894060134887695,0.0,0.4807692308,0.9213709677,0.2403846154,1.6014888286590576,0.1088740698,0.3231441048,0.5553398058,0.2160090873,0.9418768882751465,0.0007157345,0.4471544715,0.7906382979,0.223935103
-qaoa,6,6.4242448806762695,0.4182902699,0.4567901235,0.6666666667,0.4375401967,4.920807838439941,0.1144954695,0.5245901639,0.6702702703,0.3195428167,2.944206953048706,0.4571242878,0.5833333333,0.9166666667,0.52022881055,1.5889873504638672,0.0112124479,0.5128205128,0.9315789474,0.26201648034999997,1.7164299488067627,0.2846793155,0.3333333333,0.7194244604,0.3090063244,0.2595329284667969,0.1510470001,0.4126984127,0.7490039841,0.2818727064
-ae,7,3.0901079177856445,0.4359633329,0.2444444444,0.6086956522,0.34020388865,1.5193347930908203,0.0944352353,0.2857142857,0.8830083565,0.1900747605,5.446977853775024,1.525e-07,0.3289473684,0.9427710843,0.16447376045,4.39299201965332,1.35089e-05,0.362962963,0.9243697479,0.18148823595000002,0.10846900939941406,0.1826328137,0.0857142857,0.6713615023,0.1341735497,0.4256250858306885,0.2534877409,0.2688172043,0.7895927602,0.26115247259999996
-wstate,7,2.1416027545928955,0.8020354002,0.3333333333,0.8846153846,0.56768436675,2.1150600910186768,0.5691830766,0.3333333333,0.8775510204,0.45125820495,2.4824512004852295,0.7449049386,0.3333333333,0.8965517241,0.5391191359499999,1.5548548698425293,0.1632748553,0.3333333333,0.9516129032,0.2483040943,0.2084217071533203,0.6141845216,0.3333333333,0.7857142857,0.47375892745000003,0.2939729690551758,0.7644261679,0.3333333333,0.914893617,0.5488797505999999
-ghz,9,3.509758949279785,0.6730520213,0.0,0.4137931034,0.33652601065,12.370725154876709,0.6418264304,0.0,0.9436619718,0.3209132152,5.032413005828857,0.6326681137,0.0,0.4615384615,0.31633405685,1.173570156097412,0.3532048812,0.0,0.9727891156,0.1766024406,0.23156404495239258,0.7572442378,0.0,0.6,0.3786221189,0.10022997856140137,0.7678648995,0.0,0.9245283019,0.38393244975
-portfoliovqe,8,6.47698712348938,3.15548e-05,0.365356623,0.5638095238,0.1826940889,1.8811471462249756,0.0072694569,0.6547619048,0.8944723618,0.33101568085,2.2421109676361084,0.0072694569,0.6547619048,0.8944723618,0.33101568085,5.0838868618011475,0.0,0.5444444444,0.9258893281,0.2722222222,0.8158800601959229,0.0020553291,0.3852140078,0.6280752533,0.19363466845,1.4140880107879639,0.0009709346,0.4833333333,0.6753246753,0.24215213395
-vqe,11,2.3702280521392822,0.5875893381,0.3043478261,0.8844221106,0.4459685821,1.2497992515563965,0.3488519554,0.4,0.8611111111,0.3744259777,2.4040939807891846,0.3488519554,0.4,0.8611111111,0.3744259777,2.6662893295288086,0.0888363403,0.4,0.96996997,0.24441817015,0.15247535705566406,0.3858775467,0.4,0.8742138365,0.39293877335000005,0.2584071159362793,0.6254274554,0.4,0.9212598425,0.5127137277
-qpeexact,15,16.81006622314453,0.0035045018,0.5032822757,0.8538067818,0.25339338874999995,3.654416799545288,0.0,0.6556122449,0.2603773585,0.32780612245,2.872941017150879,9.5855e-06,0.6648301194,0.2754491018,0.33241985245,2.1061580181121826,0.0,0.5518341308,0.8694565896,0.2759170654,2.1409289836883545,0.0001227266,0.4375,0.4725274725,0.2188113633,7.24176812171936,1.17901e-05,0.535483871,0.7014925373,0.26774783054999995
-wstate,18,5.094771862030029,0.3666846069,0.2857142857,0.8558823529,0.3261994463,5.131527900695801,1.1e-09,0.5680473373,0.8669291339,0.2840236692,3.438722848892212,0.3254635914,0.3043478261,0.8391608392,0.31490570875,4.37470817565918,5.741e-07,0.4098360656,0.8813229572,0.20491831985,0.7653050422668457,0.2816715219,0.4411764706,0.7806451613,0.36142399625,0.3837089538574219,0.1754532795,0.4411764706,0.9137055838,0.30831487505
-twolocalrandom,3,4.069226980209351,0.7053854185,0.0,0.6538461538,0.35269270925,3.714945077896118,0.1930838903,0.0,0.9090909091,0.09654194515,9.313042879104614,0.8507321146,0.0,0.8360655738,0.4253660573,2.8050081729888916,0.1694352684,0.0,0.9333333333,0.0847176342,0.1285390853881836,0.7463039891,0.0,0.7532467532,0.37315199455,0.1242380142211914,0.4050650143,0.0,0.8235294118,0.20253250715
-dj,5,2.078658103942871,0.8840451363,0.0,0.8372093023,0.44202256815,1.8946619033813477,0.6929497365,0.24,0.5901639344,0.46647486825,15.828634262084961,0.8000719724,0.0,0.8372093023,0.4000359862,1.3755719661712646,0.1102268139,0.1578947368,0.928030303,0.13406077535,0.028011083602905273,0.5789975413,0.0,0.8,0.28949877065,0.17467689514160156,0.8858114952,0.0,0.8372093023,0.4429057476
-qpeexact,5,20.29513692855835,0.6021339195,0.1136363636,0.572815534,0.35788514154999995,1.23940110206604,0.2585171041,0.1923076923,0.8563535912,0.2254123982,2.945568084716797,0.5194816657,0.22,0.5412844037,0.36974083285,3.319031000137329,0.0003529416,0.275,0.9214916585,0.1376764708,0.1990811824798584,0.5167975066,0.0526315789,0.6041666667,0.28471454275,0.24948477745056152,0.5677390753,0.0,0.8315217391,0.28386953765
-realamprandom,4,3.0652480125427246,0.714101498,0.0,0.6829268293,0.357050749,0.023466825485229492,0.4080208817,0.2777777778,0.8363636364,0.34289932975000004,2.9806699752807617,0.4080208817,0.2777777778,0.8363636364,0.34289932975000004,3.278601884841919,0.0074010189,0.2424242424,0.9246575342,0.12491263065,0.3089442253112793,0.4924767899,0.0,0.7281553398,0.24623839495,0.22474336624145508,0.2534812171,0.0,0.8153846154,0.12674060855
-su2random,4,19.488693952560425,0.5111378888,0.0909090909,0.56,0.30102348985,0.10056400299072266,0.3831507881,0.2777777778,0.8656716418,0.33046428294999997,2.3324239253997803,0.3450234487,0.2777777778,0.8965517241,0.31140061325,2.6292710304260254,0.0005873997,0.2608695652,0.9243421053,0.13072848245,0.20556211471557617,0.4924767899,0.0,0.7358490566,0.24623839495,0.19481825828552246,0.2526484057,0.0,0.8371040724,0.12632420285
-ae,12,20.347645044326782,0.001374812,0.3595679012,0.5484320557,0.1804713566,3.7074267864227295,1.427e-06,0.6116071429,0.3475728155,0.30580428495,3.9438908100128174,0.0005164101,0.5405405405,0.501497006,0.2705284753,7.524529933929443,0.0,0.4077669903,0.9269503546,0.20388349515,3.442873239517212,0.0216237172,0.3333333333,0.6043956044,0.17747852525,4.127873182296753,0.0061049869,0.4021164021,0.7138531416,0.2041106945
-qpeexact,16,20.357758045196533,0.0004369329,0.5755287009,0.6242905789,0.2879828169,3.6968231201171875,0.0,0.6994660564,0.2622397299,0.3497330282,4.914895057678223,8.87e-08,0.6064257028,0.3187414501,0.30321289575,12.457896947860718,0.0,0.5703839122,0.9038917684,0.2851919561,1.8996970653533936,3.00399e-05,0.4760383387,0.4817880795,0.2380341893,2.45241117477417,1.38e-07,0.5540897098,0.6978876046,0.2770449239
-pricingput,5,19.063333749771118,0.457540803,0.0365853659,0.6771653543,0.24706308445,2.932431936264038,0.1401537687,0.1153846154,0.6994219653,0.12776919204999998,4.153294086456299,0.356912159,0.1229508197,0.8851224105,0.23993148935,6.883472204208374,0.0001275607,0.0549450549,0.9158186864,0.0275363078,1.8709311485290527,0.1723673112,0.0441176471,0.7312252964,0.10824247915,0.3923811912536621,0.2887562307,0.0326086957,0.8178217822,0.16068246320000001
-qgan,6,15.723639726638794,0.6570637196,0.2380952381,0.7823834197,0.44757947885,1.4241321086883545,0.3630292438,0.3950617284,0.6823529412,0.3790454861,3.3675670623779297,0.4905301095,0.3703703704,0.7313432836,0.43045023995,3.856187105178833,0.0090887718,0.3333333333,0.9343065693,0.17121105255,0.13822317123413086,0.3161444695,0.1935483871,0.7919463087,0.25484642830000004,0.26949191093444824,0.5698911586,0.2222222222,0.7540983607,0.39605669039999997
-graphstate,5,3.4352169036865234,0.8516687588,0.0909090909,0.8196721311,0.47128892485,29.82397484779358,0.0870569728,0.4615384615,0.585106383,0.27429771715,3.7099709510803223,0.7216849085,0.2,0.9166666667,0.46084245425000003,3.3216660022735596,0.040491035,0.4375,0.9262672811,0.2389955175,0.0660560131072998,0.5468772071,0.0909090909,0.7027027027,0.318893149,0.13630080223083496,0.5541593003,0.1176470588,0.7638888889,0.33590317955000004
-groundstate,12,9.675266027450562,0.0029451841,0.5424836601,0.4461538462,0.2727144221,6.524593830108643,0.0,0.708,0.8361730013,0.354,6.910717248916626,0.0004587037,0.6872246696,0.5332419465,0.34384168665,7.383896827697754,0.0,0.5235507246,0.9100391134,0.2617753623,3.2035601139068604,4.31641e-05,0.596107056,0.6608910891,0.29807511005000004,7.435669183731079,1.15459e-05,0.5649452269,0.5790513834,0.2824783864
-qgan,12,19.387936115264893,0.0117091473,0.3214285714,0.5889070147,0.16656885935,6.182060956954956,0.0,0.675,0.8404255319,0.3375,3.6683781147003174,0.0055853235,0.625,0.6043956044,0.31529266175,4.5570690631866455,0.0,0.5035460993,0.8350877193,0.25177304965,8.815797805786133,0.0047418996,0.4020618557,0.7001545595,0.20340187765,6.910181760787964,0.0115761159,0.4285714286,0.6120689655,0.22007377225000002
-ae,8,30.604209184646606,0.2236049505,0.276119403,0.7303822938,0.24986217675,3.0286240577697754,0.0450419565,0.3571428571,0.8785249458,0.20109240679999998,10.178884267807007,0.0450419565,0.3571428571,0.8785249458,0.20109240679999998,0.21062397956848145,0.0450419565,0.3571428571,0.8785249458,0.20109240679999998,0.37587666511535645,0.1735657961,0.1619047619,0.6082089552,0.16773527900000001,0.7314951419830322,0.0274389829,0.328358209,0.764084507,0.17789859595000002
-vqe,13,8.268578052520752,0.486375308,0.303030303,0.8630705394,0.39470280550000003,5.734224796295166,0.0250109336,0.5851851852,0.6064139942,0.30509805939999995,2.4400007724761963,0.5124621385,0.4333333333,0.8484848485,0.4728977359,4.712796926498413,6.2e-09,0.419047619,0.8886532344,0.2095238126,0.19640302658081055,0.4330592197,0.4166666667,0.875,0.4248629432,0.2539381980895996,0.5651758805,0.4166666667,0.9210526316,0.49092127360000004
-graphstate,12,6.764325141906738,0.5260121943,0.4583333333,0.8181818182,0.49217276379999997,5.763861894607544,0.0102722298,0.7311827957,0.3921568627,0.37072751275,5.262446165084839,0.523081853,0.5128205128,0.7547169811,0.5179511829,3.773052930831909,0.0042888072,0.5555555556,0.8474576271,0.2799221814,0.18398690223693848,0.256302079,0.4736842105,0.7054263566,0.36499314475,0.3819141387939453,0.4074608428,0.5833333333,0.8545454545,0.49539708805000005
-pricingcall,5,10.569533824920654,0.3862008176,0.0731707317,0.6974169742,0.22968577465,5.026379108428955,0.194511118,0.1438848921,0.5813253012,0.16919800505,3.7742462158203125,0.4667464399,0.0365853659,0.6510638298,0.2516659029,8.79076623916626,0.0001409961,0.1101694915,0.9266625233,0.055155243799999996,0.13372325897216797,0.1854975244,0.0909090909,0.7261410788,0.13820330765,0.31738901138305664,0.3226193645,0.0365853659,0.8225108225,0.1796023652
-qftentangled,6,12.67887806892395,0.4512393465,0.1964285714,0.908496732,0.32383395895,2.316941261291504,0.1071518908,0.4772727273,0.8456140351,0.29221230905,10.362775087356567,0.1071518908,0.4772727273,0.8456140351,0.29221230905,0.37950587272644043,8.4082e-06,0.4899328859,0.9198924731,0.24497064705,0.4366731643676758,0.1871540931,0.2777777778,0.5636363636,0.23246593545,0.40330004692077637,0.3824313592,0.2676056338,0.8044077135,0.32501849650000003
-qftentangled,11,14.432429075241089,0.0091432908,0.4857142857,0.7975578406,0.24742878825,2.9505112171173096,0.0010549127,0.6814814815,0.8457142857,0.3412681971,9.658331155776978,0.0010549127,0.6814814815,0.8457142857,0.3412681971,4.6486592292785645,2.11161e-05,0.6666666667,0.9531433034,0.3333438914,1.297656774520874,0.0027373838,0.4618181818,0.5115452931,0.2322777828,10.689543962478638,0.0005772817,0.4583333333,0.7558685446,0.22945530749999998
-qgan,13,27.22817897796631,0.0006337235,0.334841629,0.6821668265,0.16773767625,0.5448520183563232,6.2e-09,0.5607344633,0.4786450663,0.28036723474999997,4.621261119842529,0.0108759456,0.4884259259,0.5582822086,0.24965093575,13.167240142822266,0.0,0.6666666667,0.8393556813,0.33333333335,1.1946768760681152,0.0268979916,0.4439655172,0.688590604,0.2354317544,5.261607885360718,0.0040243336,0.7091503268,0.6584821429,0.3565873302
-ghz,12,2.5973050594329834,0.61477306,0.0,0.3658536585,0.30738653,0.23896384239196777,0.1364208696,0.3970588235,0.1807228916,0.26673984655,3.7797691822052,0.583466592,0.3428571429,0.3,0.46316186745,1.8335349559783936,0.0002947313,0.3191489362,0.863372093,0.15972183375000001,0.11125302314758301,0.5763532555,0.0,0.5769230769,0.28817662775,0.13970518112182617,0.717963403,0.0,0.924137931,0.3589817015
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-qpeexact,3,3.9830989837646484,0.862775187,0.0,0.75,0.4313875935,22.954224109649658,0.9372955357,0.0,0.96,0.46864776785,0.6212248802185059,0.8953283742,0.0,0.75,0.4476641871,1.6082789897918701,0.4510150569,0.0,0.9333333333,0.22550752845,0.08147907257080078,0.8885342881,0.0,0.8571428571,0.44426714405,0.06347513198852539,0.901509168,0.0,0.8823529412,0.450754584
-realamprandom,12,17.027791023254395,3.93998e-05,0.5736235595,0.2439496612,0.28683147965,8.054600954055786,0.0,0.7604938272,0.1717791411,0.3802469136,2.71763014793396,1.3914e-06,0.7172774869,0.2107438017,0.35863943915,3.916057825088501,0.0,0.4942084942,0.8634686347,0.2471042471,4.561639785766602,0.0013996192,0.625,0.7179487179,0.3131998096,7.947779893875122,1.909e-07,0.613559322,0.6087533156,0.30677975645
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-qft,4,14.332580089569092,0.7951252273,0.1851851852,0.5090909091,0.49015520625,8.15866494178772,0.4281378286,0.1666666667,0.9289940828,0.29740224765,2.328423261642456,0.6736519782,0.2424242424,0.4590163934,0.45803811029999997,2.8187458515167236,0.0024254611,0.3461538462,0.9195876289,0.17428965365000001,0.17074799537658691,0.756900508,0.2,0.5901639344,0.47845025399999996,0.14584779739379883,0.6570716887,0.0,0.856,0.32853584435
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-qft,7,8.204898834228516,0.3726106997,0.2792792793,0.4,0.3259449895,0.6191051006317139,0.0734221919,0.5098039216,0.8495575221,0.29161305675,3.419365167617798,0.2239954511,0.4728682171,0.3798076923,0.3484318341,3.082962989807129,2.11632e-05,0.4786324786,0.9217391304,0.23932682090000001,0.5196418762207031,0.1740998345,0.3404255319,0.5252525253,0.2572626832,0.4344029426574707,0.4156313388,0.2666666667,0.7945205479,0.34114900275000004
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-qpeexact,9,7.850636959075928,0.1368047044,0.3742690058,0.7385321101,0.2555368551,0.9853696823120117,0.0145873673,0.4756097561,0.8525179856,0.2450985617,4.499680995941162,0.0018435214,0.4347826087,0.9543952412,0.21831306505,0.37323498725891113,0.0145873673,0.4756097561,0.8525179856,0.2450985617,1.1707172393798828,0.095254704,0.2594936709,0.5741239892,0.17737418745,0.9264640808105469,0.1052199426,0.339869281,0.775,0.22254461180000001
-dj,9,4.5991199016571045,0.7229320316,0.0,0.7826086957,0.3614660158,2.8206381797790527,0.406972076,0.406779661,0.5496183206,0.4068758685,4.635155916213989,0.6616713239,0.28125,0.6923076923,0.47146066195,0.8346350193023682,0.5949688459,0.0,0.9157894737,0.29748442295,0.12855100631713867,0.5373251054,0.1111111111,0.75,0.32421810825,0.28714895248413086,0.4457847465,0.0,0.7472527473,0.22289237325
-qpeinexact,14,3.9899420738220215,0.0008668145,0.5033898305,0.3815513627,0.2521283225,2.730232000350952,5.1e-09,0.6694915254,0.2788388083,0.33474576525,4.4869349002838135,0.0001038688,0.634551495,0.3774560496,0.3173276819,12.126680850982666,0.0,0.5523917995,0.902325064,0.27619589975,0.8287091255187988,0.0031464084,0.4127358491,0.5426105717,0.20794112875,12.991515874862671,2.5945e-06,0.5673249551,0.7098958333,0.2836637748
-portfoliovqe,4,4.374279975891113,0.6192985515,0.0,0.8311688312,0.30964927575,2.0412228107452393,0.3378659218,0.2777777778,0.9010989011,0.30782184980000005,5.578613042831421,0.4825500185,0.2727272727,0.7053571429,0.37763864560000004,4.434314966201782,0.3378659218,0.2777777778,0.9010989011,0.30782184980000005,0.2430119514465332,0.3917342631,0.0,0.7209302326,0.19586713155,0.23128509521484375,0.2534812171,0.0,0.8172588832,0.12674060855
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-qaoa,12,10.421724081039429,0.3409165138,0.6140350877,0.905,0.47747580075,6.599740743637085,0.0002104326,0.6723163842,0.3655913978,0.33626340839999996,4.483053684234619,0.2498526372,0.688172043,0.6868686869,0.46901234010000004,3.811455011367798,8.57e-08,0.7083333333,0.9603305785,0.3541667095,0.38185811042785645,0.1593314913,0.7083333333,0.8048780488,0.4338324123,0.3658900260925293,0.1929941583,0.7083333333,0.8921348315,0.4506637458
-pricingput,9,3.8906378746032715,0.0497197281,0.2642045455,0.8772235787,0.15696213679999999,7.378418922424316,6.8598e-06,0.3270223752,0.4679487179,0.1635146175,5.811328172683716,0.0016360041,0.3366834171,0.5031210986,0.16915971059999999,2.830833911895752,0.0002973834,0.1776315789,0.8665496049,0.08896448115,0.5787980556488037,0.0157278753,0.1697416974,0.6723095526,0.09273478635,1.1840629577636719,0.0010413428,0.2540540541,0.7659709045,0.12754769845
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-su2random,8,27.097240686416626,0.0009848703,0.4781297134,0.299154334,0.23955729185,1.9258198738098145,0.0125375231,0.6547619048,0.8571428571,0.33364971395,12.864824771881104,0.0125375231,0.6547619048,0.8571428571,0.33364971395,1.2422080039978027,0.0,0.487654321,0.9201183432,0.2438271605,0.3537440299987793,0.0045483478,0.4080717489,0.5584158416,0.20631004835,1.0078153610229492,0.0009668578,0.4833333333,0.692937564,0.24215009555
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-ae,4,6.252346038818359,0.7606300416,0.0,0.8125,0.3803150208,1.9952621459960938,0.4429737214,0.0,0.9230769231,0.2214868607,5.067634344100952,0.6065791663,0.0,0.7857142857,0.30328958315,0.09856390953063965,0.1624902848,0.0952380952,0.9339622642,0.12886419,0.05037188529968262,0.7965441007,0.0,0.746031746,0.39827205035,0.12347698211669922,0.6517974857,0.0,0.8615384615,0.32589874285
-vqe,6,2.4082140922546387,0.7115673371,0.3,0.9056603774,0.50578366855,0.564751148223877,0.5875495869,0.3,0.8648648649,0.44377479345000004,4.169916868209839,0.5875495869,0.3,0.8648648649,0.44377479345000004,0.864616870880127,0.2642631585,0.3,0.9537037037,0.28213157925,0.31841492652893066,0.7640605952,0.3,0.8863636364,0.5320302976,0.13153934478759766,0.7860912609,0.3,0.9230769231,0.54304563045
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-dj,13,4.596282720565796,0.4780671891,0.0,0.8458498024,0.23903359455,1.2746038436889648,0.0090261112,0.4137931034,0.3602941176,0.2114096073,10.710082054138184,0.4300708931,0.2631578947,0.706185567,0.34661439390000004,10.298516035079956,3.3248e-06,0.2051282051,0.9271028037,0.10256576495,0.521108865737915,0.3987404964,0.037037037,0.775,0.2178887667,0.3943490982055664,0.3457513116,0.0,0.7518796992,0.1728756558
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-qpeexact,8,22.474122047424316,0.3737899576,0.180952381,0.9068322981,0.2773711693,3.2327470779418945,0.0010037051,0.4940239044,0.3480519481,0.24751380474999998,11.110357999801636,0.2294223606,0.3286713287,0.481884058,0.27904684465,1.2374250888824463,0.0346544024,0.4,0.8539325843,0.21732720120000001,0.9181981086730957,0.1438096983,0.2072072072,0.5697674419,0.17550845275,0.7159569263458252,0.2291719639,0.3307692308,0.7666068223,0.27997059734999996
-dj,8,19.988264083862305,0.7551679786,0.0,0.7922077922,0.3775839893,2.2448461055755615,0.6384115806,0.0,0.9135802469,0.3192057903,12.094086170196533,0.6384115806,0.0,0.9135802469,0.3192057903,2.435966968536377,0.023410286,0.1081081081,0.9267326733,0.06575919705,0.07294607162475586,0.6576700178,0.0714285714,0.7878787879,0.3645492946,0.29082512855529785,0.527832462,0.0,0.7894736842,0.263916231
-qpeinexact,15,18.06171703338623,0.0018973983,0.5351851852,0.6998332407,0.26854129175,6.950803756713867,3e-10,0.7076023392,0.2569832402,0.35380116975000003,4.059662818908691,0.0007533836,0.4861111111,0.3650793651,0.24343224735,4.450862884521484,0.0,0.4928909953,0.8693498452,0.24644549765,1.0854949951171875,0.0001493119,0.4887218045,0.4923664122,0.2444355582,7.3751747608184814,1.0335e-05,0.5346215781,0.7153987168,0.26731595655
-routing,2,1.2668328285217285,0.9092618063,0.0,0.9333333333,0.45463090315,7.771701097488403,0.7581056143,0.0,0.9552238806,0.37905280715,1.7563409805297852,0.9609433972,0.0,0.9333333333,0.4804716986,1.9002859592437744,0.5695137457,0.0,0.9615384615,0.28475687285,0.4166109561920166,0.9251305142,0.0,0.85,0.4625652571,0.0582888126373291,0.9160040437,0.0,0.8928571429,0.45800202185
-qft,6,23.294311046600342,0.506159198,0.0952380952,0.8436724566,0.3006986466,9.173959970474243,0.0198367936,0.4416666667,0.3650793651,0.23075173015,1.7417287826538086,0.1361749733,0.4615384615,0.8488372093,0.29885671739999997,3.614074945449829,0.0004942518,0.3333333333,0.9221902017,0.16691379255,0.27364301681518555,0.3128855273,0.2258064516,0.5338345865,0.26934598945,0.3403050899505615,0.4187099203,0.2456140351,0.8161290323,0.3321619777
-ghz,4,3.7421820163726807,0.8742725085,0.0,0.7,0.43713625425,30.63805890083313,0.9221571624,0.0,0.75,0.4610785812,2.7240092754364014,0.9337071418,0.0,0.7,0.4668535709,1.9262809753417969,0.6702586186,0.0,0.9736842105,0.3351293093,0.02370285987854004,0.8886287937,0.0,0.7,0.44431439685,0.044287919998168945,0.8945129678,0.0,0.9268292683,0.4472564839
-dj,11,4.382766008377075,0.6103244713,0.0,0.7718120805,0.30516223565,3.3979320526123047,0.0255118518,0.4768211921,0.6378896882,0.25116652195,9.367262840270996,0.5830621582,0.1891891892,0.6636363636,0.38612567369999995,2.423520088195801,0.5276979246,0.0,0.9130434783,0.2638489623,0.509260892868042,0.5371177751,0.037037037,0.7272727273,0.28707740605000004,0.32071828842163086,0.3966746869,0.0,0.75,0.19833734345
-qft,5,2.351586103439331,0.6750350956,0.1842105263,0.525,0.42962281095,3.378998041152954,0.2626137426,0.3461538462,0.8514285714,0.30438379439999996,1.5596661567687988,0.2626137426,0.3461538462,0.8514285714,0.30438379439999996,3.3705179691314697,0.0024481065,0.2857142857,0.9224376731,0.1440811961,0.174454927444458,0.2360078731,0.1621621622,0.6021505376,0.19908501764999997,0.2550687789916992,0.3121322725,0.1428571429,0.8387096774,0.2274947077
-wstate,6,2.1675171852111816,0.7127064716,0.3,0.8969072165,0.5063532358,3.3603157997131348,0.7776272933,0.3,0.8969072165,0.53881364665,2.7384397983551025,0.6678182454,0.2,0.7422680412,0.43390912270000004,2.722038984298706,0.2625365499,0.3,0.9516908213,0.28126827495,0.10710000991821289,0.7769914656,0.3,0.7872340426,0.5384957328,0.13796401023864746,0.788440806,0.3,0.9152542373,0.544220403
-ghz,14,2.351792812347412,0.5606727137,0.0,0.4047619048,0.28033635685,11.17099118232727,0.0767385918,0.4366197183,0.1931818182,0.25667915505,2.9228639602661133,0.5973502161,0.1785714286,0.3777777778,0.38796082235,4.701438903808594,0.0002214968,0.0,0.9692898273,0.0001107484,0.21531176567077637,0.5499361727,0.0,0.5666666667,0.27496808635,0.16317296028137207,0.6506758605,0.0,0.9239766082,0.32533793025
-pricingput,11,23.063773155212402,0.0002656367,0.1803921569,0.592217484,0.09032889679999999,1.916684865951538,1.68152e-05,0.2475728155,0.8661468486,0.12379481534999999,5.815353870391846,0.0065153521,0.223655914,0.6920529801,0.11508563305000001,9.95284390449524,2.5e-09,0.2401960784,0.9586709887,0.12009804045000001,6.711122989654541,0.0001840318,0.1979166667,0.664628821,0.09905034925,1.715397834777832,0.0003436182,0.2760736196,0.7702067669,0.1382086189
-su2random,2,1.8516991138458252,0.9200288191,0.0,0.9433962264,0.46001440955,11.194967985153198,0.9363309001,0.0,0.9333333333,0.46816545005,3.6116981506347656,0.9363309001,0.0,0.9433962264,0.46816545005,3.371506929397583,0.5074563923,0.0,0.9651162791,0.25372819615,0.245070219039917,0.9243693585,0.0,0.9,0.46218467925,0.0508267879486084,0.9155385386,0.0,0.9189189189,0.4577692693
-qftentangled,7,2.897796154022217,0.1873288308,0.3798449612,0.3886255924,0.283586896,3.1454451084136963,0.0551271582,0.5263157895,0.846361186,0.29072147385,3.4013960361480713,0.2100375363,0.4814814815,0.3778801843,0.34575950889999996,3.9477338790893555,9.612e-07,0.4743589744,0.9206510682,0.2371799678,0.40706610679626465,0.1582791998,0.3392857143,0.4790697674,0.24878245705,0.527393102645874,0.3371482762,0.325203252,0.7634615385,0.3311757641
-qpeexact,2,1.8258438110351562,0.9418008285,0.0,0.8461538462,0.47090041425,6.370228052139282,0.893710882,0.0,0.9428571429,0.446855441,1.8522920608520508,0.9645415489,0.0,0.8461538462,0.48227077445,2.1619529724121094,0.7815702749,0.0,0.9428571429,0.39078513745,0.02974104881286621,0.9717261763,0.0,0.9166666667,0.48586308815,0.027580976486206055,0.9803381684,0.0,0.9230769231,0.4901690842
-qftentangled,7,21.73518395423889,0.2548869561,0.4487179487,0.910652921,0.35180245239999997,4.932585000991821,0.0551271582,0.5263157895,0.846361186,0.29072147385,7.5276570320129395,0.1378340443,0.5510204082,0.3900414938,0.34442722625,4.780981063842773,2.909e-07,0.4379084967,0.9207253886,0.21895439379999998,0.4700448513031006,0.1582791998,0.3392857143,0.4814814815,0.24878245705,0.4997539520263672,0.3371482762,0.325203252,0.7634615385,0.3311757641
-qftentangled,10,6.906783103942871,0.0002379582,0.557496361,0.1879432624,0.2788671596,4.605191946029663,8.55087e-05,0.6666666667,0.26448737,0.3333760877,6.590417861938477,0.0030790827,0.6578947368,0.8453188602,0.33048690975,5.576344966888428,0.0001554327,0.6363636364,0.952173913,0.31825953455,3.3689467906951904,0.029946152,0.3628318584,0.5201698514,0.1963890052,6.376370906829834,0.0021553373,0.4082397004,0.7543698252,0.20519751885
-qgan,12,39.338752031326294,0.055873213,0.4123376623,0.7993485342,0.23410543765,26.570784091949463,9.786e-07,0.5555555556,0.5169491525,0.27777826710000003,31.961019277572632,0.0055521958,0.625,0.6220472441,0.3152760979,1.9929420948028564,0.0,0.5085470085,0.8340425532,0.25427350425,2.5811049938201904,0.0047418996,0.4020618557,0.7010785824,0.20340187765,6.998058080673218,0.0115761159,0.4285714286,0.6120689655,0.22007377225000002
-ghz,13,4.761702299118042,0.1967113386,0.3888888889,0.25,0.29280011375000004,18.014827013015747,0.0,0.4468085106,0.8967032967,0.2234042553,5.951834201812744,0.4404999165,0.4,0.2857142857,0.42024995825,4.890794038772583,0.0003764393,0.2727272727,0.8663967611,0.136551856,0.33136725425720215,0.5593036276,0.0,0.5714285714,0.2796518138,0.15925002098083496,0.6657417059,0.0,0.9240506329,0.33287085295
-vqe,12,4.9089319705963135,0.5794706935,0.5357142857,0.8727272727,0.5575924896,4.47072172164917,0.0316538089,0.5955882353,0.5853658537,0.31362102210000004,6.890520095825195,0.6029498905,0.4090909091,0.8971962617,0.5060203998,5.19167685508728,0.0091896975,0.3928571429,0.9278350515,0.2010234202,3.6308679580688477,0.4520052108,0.4090909091,0.8698224852,0.43054805995,0.2563607692718506,0.6271100233,0.4090909091,0.9205776173,0.5181004661999999
-graphstate,13,22.920069932937622,0.4735316953,0.4705882353,0.9039548023,0.4720599653,13.551939964294434,0.0023607945,0.6390243902,0.3880597015,0.32069259235,7.475405216217041,0.5797660366,0.6129032258,0.8074534161,0.5963346312,3.6195249557495117,0.0042877729,0.3720930233,0.8436363636,0.1881903981,0.23537993431091309,0.5050692111,0.4571428571,0.7265625,0.4811060341,0.36009716987609863,0.5328690593,0.52,0.8611111111,0.5264345296499999
-qgan,13,94.68015575408936,0.0,0.4226190476,0.8750929368,0.2113095238,5.469089031219482,1.15e-08,0.5656984786,0.4580209895,0.28284924505,8.077311992645264,0.0054679068,0.5089605735,0.5226689478,0.25721424015,2.888932228088379,0.0,0.4465408805,0.8327196213,0.22327044025,5.099527835845947,0.0269180054,0.4439655172,0.6830601093,0.2354417613,5.298359155654907,0.0040243336,0.7091503268,0.6584821429,0.3565873302
-ae,9,56.380772829055786,6e-10,0.4214285714,0.8927203065,0.210714286,3.353628158569336,0.0504916348,0.5064935065,0.4945295405,0.27849257065,6.676017761230469,0.1072299088,0.4098360656,0.7127158556,0.2585329872,1.9378399848937988,0.0017280573,0.4166666667,0.9519679787,0.209197362,0.22194886207580566,0.1924007782,0.2735042735,0.6785714286,0.23295252585,0.7243032455444336,0.0177423022,0.3218390805,0.7606602476,0.16979069135
-qgan,7,33.93711018562317,0.1549574334,0.3133333333,0.4163424125,0.23414538335,6.2936110496521,0.2734795125,0.4761904762,0.90625,0.37483499435,4.199399948120117,0.3544535083,0.4615384615,0.7078651685,0.4079959849,0.6566941738128662,0.2734795125,0.4761904762,0.90625,0.37483499435,0.509631872177124,0.5955346798,0.2888888889,0.7857142857,0.44221178434999997,0.4333360195159912,0.161489261,0.3333333333,0.7625,0.24741129715
-routing,6,4.647843837738037,0.6833131305,0.4,0.8936170213,0.54165656525,3.7614381313323975,0.4575040622,0.4,0.8571428571,0.42875203110000004,30.113347053527832,0.7394861427,0.4,0.8936170213,0.56974307135,1.8298790454864502,0.1399365682,0.4,0.9509803922,0.2699682841,0.5338008403778076,0.7065244795,0.4,0.8717948718,0.55326223975,0.2781050205230713,0.742389245,0.4,0.9171270718,0.5711946225
-graphstate,4,7.39665675163269,0.853929258,0.0,0.8510638298,0.426964629,3.919475793838501,0.7618269473,0.375,0.7142857143,0.56841347365,1.883876085281372,0.7703322835,0.25,0.9166666667,0.5101661417500001,2.5314061641693115,0.6149045542,0.25,0.9701492537,0.4324522771,0.1607508659362793,0.7419241544,0.0,0.7586206897,0.3709620772,0.1998598575592041,0.6673389511,0.1,0.8245614035,0.38366947554999997
-su2random,5,6.221367120742798,0.5144014549,0.1481481481,0.564516129,0.3312748015,2.1770009994506836,0.1860281465,0.4333333333,0.8823529412,0.3096807399,27.428030967712402,0.1860281465,0.4333333333,0.8823529412,0.3096807399,2.0560598373413086,0.1860281465,0.4333333333,0.8823529412,0.3096807399,1.079301118850708,0.465651066,0.1285714286,0.6022727273,0.2971112473,0.42696285247802734,0.1229713869,0.2528735632,0.7629427793,0.18792247504999998
-ae,13,34.66151285171509,0.0,0.4288747346,0.9063432094,0.2144373673,3.9509479999542236,6.29473e-05,0.6075533662,0.401179941,0.30380815675,8.517568111419678,0.0013065685,0.6009852217,0.401179941,0.3011458951,2.2783539295196533,0.0,0.4376590331,0.8755935423,0.21882951655,2.0742080211639404,0.0018877746,0.4046052632,0.6031331593,0.2032465189,3.6171529293060303,0.0001037794,0.5905511811,0.7453208556,0.29532748025
-twolocalrandom,2,2.379883050918579,0.9434226178,0.0,0.9333333333,0.4717113089,0.09646844863891602,0.4874264289,0.0,0.9659090909,0.24371321445,1.612886905670166,0.9363309001,0.0,0.9333333333,0.46816545005,8.605350255966187,0.6322873591,0.0,0.9615384615,0.31614367955,0.0566411018371582,0.9251305142,0.0,0.85,0.4625652571,0.03232312202453613,0.9160040437,0.0,0.8928571429,0.45800202185
-dj,4,3.995504856109619,0.9293600345,0.0,0.8928571429,0.46468001725,7.560328006744385,0.6433160392,0.0,0.9464285714,0.3216580196,3.2126410007476807,0.8612242052,0.25,0.6756756757,0.5556121026,3.2906291484832764,0.3106630558,0.0,0.9323308271,0.1553315279,5.409952163696289,0.6457269088,0.0,0.875,0.3228634544,0.04436206817626953,0.6445913311,0.0,0.9032258065,0.32229566555
-qpeexact,4,8.961036205291748,0.8276160522,0.0,0.7021276596,0.4138080261,2.6167149543762207,0.7599154189,0.1304347826,0.5964912281,0.44517510075,10.47310996055603,0.7856717172,0.0,0.6666666667,0.3928358586,2.689265012741089,0.1475342901,0.0,0.9141414141,0.07376714505,0.5444931983947754,0.8320699021,0.0,0.7962962963,0.41603495105,0.13004064559936523,0.800201868,0.1,0.8717948718,0.450100934
-realamprandom,5,7.4323859214782715,0.4922859189,0.2528735632,0.4941860465,0.37257974105,2.101180076599121,0.2294145094,0.4333333333,0.8285714286,0.33137392135,12.707585096359253,0.2294145094,0.4333333333,0.8285714286,0.33137392135,2.004626989364624,0.2294145094,0.4333333333,0.8285714286,0.33137392135,0.6130459308624268,0.465651066,0.1285714286,0.6,0.2971112473,0.44423508644104004,0.1233263237,0.2528735632,0.75,0.18809994345
-portfoliovqe,9,6.994858980178833,0.007505298,0.5578231293,0.3663793103,0.28266421365,1.0553512573242188,0.0018105829,0.6944444444,0.8938053097,0.34812751365,5.704023122787476,0.0018105829,0.6944444444,0.8938053097,0.34812751365,1.431617021560669,4.19249e-05,0.6944444444,0.95599022,0.34724318465,0.5917959213256836,0.0118603705,0.5014749263,0.6727799228,0.2566676484,1.2334027290344238,0.0002430995,0.4909560724,0.6686643836,0.24559958595
-vqe,10,6.992198705673218,0.6798403053,0.2083333333,0.8695652174,0.4440868193,0.9724280834197998,0.3871879514,0.3888888889,0.8615384615,0.38803842015,15.470432996749878,0.5636905177,0.3333333333,0.8441558442,0.44851192549999996,2.1844091415405273,0.1129922811,0.3888888889,0.97,0.250940585,0.42668962478637695,0.4528880164,0.3888888889,0.8767123288,0.42088845264999997,0.22883820533752441,0.6696766899,0.3888888889,0.9217391304,0.5292827894
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-qaoa,8,7.101873159408569,0.3521375965,0.6875,0.9166666667,0.51981879825,4.303078889846802,0.0367376048,0.5833333333,0.6142857143,0.31003546905,2.2579188346862793,0.3521375965,0.6875,0.9166666667,0.51981879825,1.5090560913085938,0.0148493688,0.6875,0.9384615385,0.3511746844,0.2984020709991455,0.2665893366,0.66,0.7252747253,0.4632946683,0.3750178813934326,0.1990709972,0.7076923077,0.803030303,0.45338165245
-ghz,14,7.041659116744995,0.496194979,0.3870967742,0.3541666667,0.4416458766,1.00913405418396,0.0492129521,0.4615384615,0.1574074074,0.2553757068,4.215148210525513,0.5446182023,0.12,0.4047619048,0.33230910115,2.6200618743896484,0.0002985854,0.0882352941,0.9474497682,0.04426693975,0.19342589378356934,0.5499361727,0.0,0.5666666667,0.27496808635,0.1434800624847412,0.6506758605,0.0,0.9239766082,0.32533793025
-su2random,5,2.1712419986724854,0.3021109576,0.1585365854,0.5089820359,0.2303237715,0.9742021560668945,0.2177009508,0.4333333333,0.8461538462,0.32551714205,3.14695405960083,0.2177009508,0.4333333333,0.8461538462,0.32551714205,1.8191308975219727,0.2177009508,0.4333333333,0.8461538462,0.32551714205,0.30324482917785645,0.4657627537,0.1285714286,0.5977011494,0.29716709115,0.3852827548980713,0.1229713869,0.2528735632,0.7629427793,0.18792247504999998
-qpeexact,5,20.45200777053833,0.6741929691,0.0,0.8996763754,0.33709648455,2.511563777923584,0.2585171041,0.1923076923,0.8563535912,0.2254123982,2.3140039443969727,0.6696786103,0.22,0.5412844037,0.44483930515,0.4718780517578125,0.0041685828,0.25,0.9233926129,0.1270842914,0.1285710334777832,0.2962042728,0.0526315789,0.6082474227,0.17441792585,0.2724030017852783,0.5678933986,0.0,0.8333333333,0.2839466993
-vqe,7,4.921667098999023,0.771040729,0.2,0.8818897638,0.4855203645,5.329095125198364,0.5635473348,0.3333333333,0.9245283019,0.44844033404999994,1.1235439777374268,0.7434411804,0.3333333333,0.9032258065,0.53838725685,1.4396088123321533,0.1548983843,0.3333333333,0.953125,0.24411585879999997,0.11490702629089355,0.6035129981,0.3333333333,0.8787878788,0.4684231657,0.1344461441040039,0.7620116242,0.3333333333,0.923566879,0.54767247875
-tsp,9,3.4601311683654785,0.4569182183,0.6,0.9284436494,0.5284591091499999,9.56101393699646,0.085437508,0.6,0.9054373522,0.34271875399999996,3.2059547901153564,0.4527589385,0.6,0.9284436494,0.52637946925,0.9752998352050781,0.085437508,0.6,0.9054373522,0.34271875399999996,0.2608680725097656,0.2970586081,0.6,0.8791540785,0.44852930405,0.4098961353302002,0.529253429,0.6,0.9175257732,0.5646267145
-ghz,13,5.313439130783081,0.4738570559,0.1111111111,0.3720930233,0.2924840835,8.074725866317749,0.0037533489,0.3076923077,0.8712871287,0.1557228283,4.668368816375732,0.5414686095,0.1,0.347826087,0.32073430475,1.5585520267486572,0.0002287205,0.0,0.8672566372,0.00011436025,0.07816815376281738,0.5593036276,0.0,0.5714285714,0.2796518138,0.14404892921447754,0.6657417059,0.0,0.9240506329,0.33287085295
-ghz,12,11.134581089019775,0.5212704364,0.0,0.46875,0.2606352182,3.3324170112609863,0.1299001153,0.4225352113,0.1744186047,0.27621766329999997,4.34843111038208,0.507476872,0.243902439,0.2678571429,0.3756896555,1.2544760704040527,0.0119464459,0.0,0.8669724771,0.00597322295,0.09104108810424805,0.5763532555,0.0,0.5769230769,0.28817662775,0.13362908363342285,0.717963403,0.0,0.924137931,0.3589817015
-su2random,4,9.753955841064453,0.6316969799,0.0,0.6829268293,0.31584848995,2.7238380908966064,0.3912676532,0.2777777778,0.8571428571,0.3345227155,1.8670783042907715,0.3912676532,0.2777777778,0.8571428571,0.3345227155,1.8614473342895508,0.0004274902,0.2777777778,0.9253112033,0.139102634,0.16182518005371094,0.4924767899,0.0,0.7358490566,0.24623839495,0.21803689002990723,0.2526484057,0.0,0.8371040724,0.12632420285
-qpeexact,4,3.4974441528320312,0.8381237658,0.0,0.7083333333,0.4190618829,3.489964008331299,0.1421077812,0.4189189189,0.6810344828,0.28051335005,3.3424787521362305,0.7885618649,0.0,0.6666666667,0.39428093245,0.6485538482666016,0.1853469467,0.0,0.9285714286,0.09267347335,0.09190201759338379,0.706788623,0.0,0.7962962963,0.3533943115,0.14490699768066406,0.7996933519,0.1,0.8780487805,0.44984667595
-vqe,6,1.3753809928894043,0.776801253,0.3,0.9056603774,0.5384006265,1.2832579612731934,0.5875495869,0.3,0.8648648649,0.44377479345000004,2.060920000076294,0.5875495869,0.3,0.8648648649,0.44377479345000004,0.4167017936706543,0.1916206491,0.3,0.9537037037,0.24581032455000001,0.05274200439453125,0.7640605952,0.3,0.8863636364,0.5320302976,0.12598919868469238,0.7860912609,0.3,0.9230769231,0.54304563045
-qaoa,9,1.3755059242248535,0.3574362129,0.6833333333,0.7183098592,0.5203847731,0.2635791301727295,0.309066152,0.6666666667,0.9166666667,0.48786640935,1.1181697845458984,0.309066152,0.6666666667,0.9166666667,0.48786640935,0.03777813911437988,0.309066152,0.6666666667,0.9166666667,0.48786640935,0.313457727432251,0.2484313779,0.6666666667,0.7313432836,0.4575490223,0.42380499839782715,0.2084259073,0.6140350877,0.8389830508,0.4112304975
-ghz,15,1.8277759552001953,0.5172206336,0.1153846154,0.4090909091,0.3163026245,0.8673679828643799,0.0,0.4525139665,0.8593872742,0.22625698325,4.103558301925659,0.4653050079,0.1875,0.36,0.32640250395000003,0.7989919185638428,0.4747675871,0.2926829268,0.9147609148,0.38372525695000004,0.16864323616027832,0.528643537,0.0,0.5625,0.2643217685,0.15856289863586426,0.4776174386,0.0,0.9239130435,0.2388087193
-qgan,7,3.9368441104888916,0.554035236,0.2807017544,0.641509434,0.4173684952,0.7395501136779785,0.2405256715,0.4761904762,0.9230769231,0.35835807385,2.3294572830200195,0.355277215,0.4615384615,0.685483871,0.40840783825,2.920335054397583,0.0001429254,0.5490196078,0.9285213735,0.27458126660000004,0.6537718772888184,0.5959064417,0.2888888889,0.775,0.44239766529999996,0.3765099048614502,0.161489261,0.3333333333,0.7625,0.24741129715
-vqe,14,3.0016839504241943,0.4926657643,0.3404255319,0.8265682657,0.4165456481,0.7970950603485107,0.0105154284,0.5973154362,0.600536193,0.3039154323,4.099756956100464,0.2721401074,0.35,0.7368421053,0.31107005369999996,3.5396809577941895,2.921e-07,0.4225352113,0.9015256588,0.2112677517,0.23610305786132812,0.4216582234,0.4230769231,0.8737864078,0.42236757324999996,0.27179598808288574,0.5459642625,0.4230769231,0.9212121212,0.4845205928
-graphstate,15,3.2783401012420654,0.4605093964,0.6285714286,0.8813559322,0.5445404125,0.7262358665466309,0.0011077279,0.7491749175,0.3311258278,0.3751413227,3.2744922637939453,0.408995191,0.6904761905,0.7627118644,0.54973569075,2.145998001098633,3.28858e-05,0.5138888889,0.8481012658,0.25696088735,0.3782339096069336,0.3202787225,0.7209302326,0.6504065041,0.52060447755,0.43545103073120117,0.33742026,0.5897435897,0.8177570093,0.46358192485
-qpeinexact,6,2.1727399826049805,0.3944990757,0.196969697,0.5479452055,0.29573438635,8.805763244628906,0.0076614594,0.4508928571,0.7248157248,0.22927715825,3.2797799110412598,0.332426083,0.2708333333,0.4576271186,0.30162970815,3.174232244491577,9.4106e-05,0.3611111111,0.9215686275,0.18060260855,0.09522891044616699,0.3749037543,0.1153846154,0.6312056738,0.24514418485,0.32114481925964355,0.4739692783,0.15,0.8039215686,0.31198463915
-twolocalrandom,9,1.0841162204742432,0.0010648318,0.5532879819,0.3,0.27717640685,1.7243058681488037,0.0056162499,0.6944444444,0.8153846154,0.35003034715,1.9021499156951904,0.0056162499,0.6944444444,0.8153846154,0.35003034715,4.086327075958252,4.19249e-05,0.6944444444,0.95599022,0.34724318465,3.361823081970215,0.0389318669,0.4771784232,0.7028360049,0.25805514505,1.227396011352539,0.0002424848,0.4909560724,0.6720338983,0.2455992786
-realamprandom,6,7.633360147476196,0.1439000794,0.3594771242,0.7567567568,0.25168860179999997,2.435760974884033,0.112064106,0.5333333333,0.8235294118,0.32269871964999997,2.4160819053649902,0.112064106,0.5333333333,0.8235294118,0.32269871964999997,5.637089252471924,1.767e-07,0.4139784946,0.9211195929,0.20698933565000002,1.4196879863739014,0.0254417654,0.2976190476,0.7181208054,0.1615304065,0.4622018337249756,0.1941960161,0.3515151515,0.6950092421,0.2728555838
-dj,18,4.668268918991089,0.3622382717,0.0172413793,0.8361581921,0.18973982550000001,5.8016579151153564,4.57124e-05,0.4643734644,0.2783687943,0.2322095884,3.863584041595459,0.2458102632,0.3673469388,0.5967078189,0.306578601,3.7520759105682373,5.07e-08,0.2058823529,0.8904991948,0.1029412018,0.0959320068359375,0.3428141427,0.0222222222,0.748603352,0.18251818244999998,0.4999370574951172,0.2462390294,0.186440678,0.7035175879,0.2163398537
-portfoliovqe,10,13.778378963470459,0.0094317752,0.4540441176,0.5661881978,0.2317379464,1.8941650390625,0.000379652,0.7259259259,0.8932806324,0.36315278895,12.127613067626953,0.000379652,0.7259259259,0.8932806324,0.36315278895,1.836543083190918,3.7219e-06,0.7259259259,0.9554455446,0.3629648239,0.5135440826416016,8.68481e-05,0.4223744292,0.6099732858,0.21123063865,6.692779779434204,1.14331e-05,0.6026365348,0.6448160535,0.30132398395
-pricingput,5,4.331503868103027,0.1995427337,0.088,0.584717608,0.14377136685,4.21452784538269,0.1012255589,0.1290322581,0.593442623,0.11512890849999999,2.973942995071411,0.3325517356,0.07,0.6441281139,0.2012758678,1.3845980167388916,0.060974408,0.0576923077,0.8690176322,0.059333357850000004,0.13635921478271484,0.1720459835,0.0441176471,0.7333333333,0.1080818153,0.33905577659606934,0.2883244268,0.0326086957,0.8196078431,0.16046656125
-su2random,3,4.110747814178467,0.6480782915,0.0,0.7,0.32403914575,28.561532974243164,2.22887e-05,0.2631578947,0.9083601286,0.1315900917,2.4161760807037354,0.8507321146,0.0,0.8360655738,0.4253660573,2.139902114868164,0.5559371835,0.0,0.9166666667,0.27796859175,0.1239781379699707,0.7461347737,0.0,0.7710843373,0.37306738685,0.13733196258544922,0.4045669882,0.0,0.8372093023,0.2022834941
-qpeexact,11,21.305816888809204,0.0348106276,0.31640625,0.8818097876,0.1756084388,0.09868597984313965,0.0016167799,0.544,0.8513674197,0.27280838995,2.8731489181518555,0.0016167799,0.544,0.8513674197,0.27280838995,3.149482011795044,6.22924e-05,0.5137614679,0.9524017467,0.25691188015,1.564720869064331,0.0005898952,0.3460076046,0.5542372881,0.17329874990000002,10.123840093612671,0.0035094687,0.4671814672,0.7636861314,0.23534546795
-dj,8,6.900376796722412,0.7019462142,0.1578947368,0.7323943662,0.4299204755,4.471714019775391,0.2095041394,0.4426229508,0.5763888889,0.3260635451,3.157684803009033,0.6384115806,0.0,0.9135802469,0.3192057903,0.08783388137817383,0.005068078,0.5245901639,0.9221938776,0.26482912095,0.3076932430267334,0.6576700178,0.0714285714,0.7878787879,0.3645492946,0.2974100112915039,0.527832462,0.0,0.7894736842,0.263916231
-portfoliovqe,8,6.717941045761108,0.0238874024,0.4166666667,0.6405529954,0.22027703455,1.8025720119476318,0.0072694569,0.6547619048,0.8944723618,0.33101568085,4.205539226531982,0.0072694569,0.6547619048,0.8944723618,0.33101568085,0.8673610687255859,0.0,0.4950980392,0.9267241379,0.2475490196,0.9840352535247803,0.0020553291,0.3852140078,0.6280752533,0.19363466845,1.0500741004943848,0.0009709346,0.4833333333,0.6753246753,0.24215213395
-twolocalrandom,4,12.28547215461731,0.735330303,0.0,0.6538461538,0.3676651515,0.030272960662841797,0.4080208817,0.2777777778,0.8363636364,0.34289932975000004,3.7133431434631348,0.6517268063,0.0,0.6538461538,0.32586340315,2.9106979370117188,0.0012295216,0.253968254,0.925,0.1275988878,0.25931596755981445,0.4924767899,0.0,0.7281553398,0.24623839495,0.2116868495941162,0.2534812171,0.0,0.8153846154,0.12674060855
-dj,2,2.7334859371185303,0.9661062366,0.0,0.9285714286,0.4830531183,75.85603189468384,0.9764074049,0.0,0.96875,0.48820370245,1.1977059841156006,0.9506992237,0.0,0.9285714286,0.47534961185,0.7878611087799072,0.7721816753,0.0,0.96,0.38609083765,0.01914811134338379,0.9673958691,0.0,0.9,0.48369793455,0.02562880516052246,0.9449645073,0.0,0.9230769231,0.47248225365
-realamprandom,3,2.575016975402832,0.617169928,0.0,0.7083333333,0.308584964,39.04253911972046,0.2244536847,0.0,0.9090909091,0.11222684235,3.2896599769592285,0.8507321146,0.0,0.8360655738,0.4253660573,2.93416690826416,0.021710708,0.0,0.8961038961,0.010855354,0.20462584495544434,0.7463039891,0.0,0.7564102564,0.37315199455,0.11773896217346191,0.4050650143,0.0,0.8235294118,0.20253250715
-qpeexact,2,2.6871838569641113,0.9676601659,0.0,0.8461538462,0.48383008295,62.370654821395874,0.8682938381,0.0,0.9756097561,0.43414691905,0.9264998435974121,0.9592822771,0.0,0.8461538462,0.47964113855,1.551210880279541,0.7815702749,0.0,0.9428571429,0.39078513745,0.30231714248657227,0.9665999393,0.0,0.9285714286,0.48329996965,0.026278257369995117,0.9803381684,0.0,0.9230769231,0.4901690842
-qpeexact,12,23.62843370437622,0.0141701614,0.5346756152,0.7889518414,0.2744228883,2.456516742706299,6.91e-08,0.5607196402,0.2904255319,0.28035985465,4.5620880126953125,0.0014726272,0.4380664653,0.4510779436,0.21976954625,2.550973892211914,0.0,0.4432717678,0.8704716336,0.2216358839,2.2378621101379395,0.0144148885,0.3123028391,0.5115562404,0.1633588638,7.011013984680176,0.0006571002,0.4535519126,0.7184615385,0.2271045064
-vqe,16,2.820211172103882,0.4218213908,0.3541666667,0.8421052632,0.38799402875,2.986562967300415,0.0012351377,0.5259259259,0.4866920152,0.2635805318,2.306230068206787,0.3937992407,0.375,0.8421052632,0.38439962034999997,2.0237009525299072,8.5073e-06,0.5,0.9080779944,0.25000425365,0.46668481826782227,0.2869059525,0.4333333333,0.8717948718,0.3601196429,0.33371591567993164,0.284808517,0.4333333333,0.9208443272,0.35907092515
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-pricingcall,11,4.036120176315308,0.0108160034,0.2718446602,0.6591661152,0.1413303318,0.4211270809173584,1.97815e-05,0.2475728155,0.8605280975,0.1237962985,3.2157700061798096,0.0002133461,0.3769559033,0.4658054711,0.1885846247,5.571751117706299,5.6e-09,0.2268041237,0.9591664913,0.11340206465,2.4651851654052734,0.0003311534,0.1950549451,0.664825046,0.09769304925,1.5552470684051514,0.0006903313,0.2803738318,0.7868525896,0.14053208155
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-ghz,8,2.855569839477539,0.7179984916,0.2307692308,0.4583333333,0.4743838612,1.5758311748504639,0.6623946388,0.0,0.65,0.3311973194,2.019207715988159,0.5533133127,0.3529411765,0.2444444444,0.4531272446,2.3127710819244385,0.2624281083,0.0,0.9263157895,0.13121405415,0.03372478485107422,0.731581848,0.0,0.6111111111,0.365790924,0.09290218353271484,0.8023891793,0.0,0.9247311828,0.40119458965
-portfolioqaoa,5,10.87790584564209,0.2709518109,0.1818181818,0.9167367536,0.22638499634999998,2.045509099960327,0.1450327292,0.4333333333,0.9142857143,0.28918303125,4.351558208465576,0.2992562148,0.188034188,0.5263157895,0.24364520139999998,7.862702131271362,5.6e-09,0.3981481481,0.9206465834,0.19907407685,0.12064695358276367,0.3144585079,0.1153846154,0.5702479339,0.21492156165,0.6530449390411377,0.0568210927,0.2478632479,0.8023648649,0.1523421703
-dj,20,3.330592155456543,0.1888734216,0.2446808511,0.628458498,0.21677713635,3.385542869567871,0.0010902914,0.4901185771,0.3859223301,0.24560443425,1719.134034872055,0.0,0.2164948454,0.8849347568,0.1082474227,2.0608997344970703,2.0887e-06,0.3164556962,0.8890449438,0.15822889245000002,0.08826398849487305,0.1235654819,0.0175438596,0.7,0.07055467074999999,0.5816707611083984,0.1740757171,0.2117647059,0.6458333333,0.1929202115
-dj,19,3.144563913345337,0.3487245235,0.0158730159,0.671875,0.1822987697,4.177062749862671,0.0097617144,0.4193548387,0.518134715,0.21455827655,3.697288990020752,0.2805986599,0.2873563218,0.5972222222,0.28397749085,6.467382907867432,0.0,0.4406779661,0.8706140351,0.22033898305,0.7188138961791992,0.2069882012,0.0545454545,0.6961325967,0.13076682785,0.7308139801025391,0.0822722916,0.2688172043,0.6141078838,0.17554474795
-ae,2,2.4866819381713867,0.8667417848,0.0,0.9259259259,0.4333708924,14.063183069229126,0.9564709992,0.0,0.9310344828,0.4782354996,4.85900092124939,0.8828657695,0.0,0.9259259259,0.44143288475,2.363996982574463,0.6764281401,0.0,0.9591836735,0.33821407005,0.022352933883666992,0.9408179515,0.0,0.8888888889,0.47040897575,0.02825307846069336,0.9250258398,0.0,0.9166666667,0.4625129199
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-vqe,14,4.74426007270813,0.4626405721,0.4545454545,0.8053097345,0.4585930133,2.8985490798950195,0.0086298387,0.4850299401,0.5728900256,0.2468298894,4.106830835342407,0.3922751208,0.2682926829,0.8452830189,0.33028390185,7.069056987762451,0.0005050322,0.2285714286,0.9253731343,0.1145382304,0.34853339195251465,0.4216582234,0.4230769231,0.8737864078,0.42236757324999996,0.4509470462799072,0.5459642625,0.4230769231,0.9212121212,0.4845205928
-su2random,10,8.314096927642822,0.0005241195,0.4738562092,0.7937310415,0.23719016434999998,4.801918983459473,0.0010276288,0.7259259259,0.8474576271,0.36347677735,81.47129321098328,0.0010276288,0.7259259259,0.8474576271,0.36347677735,2.768543004989624,3.7219e-06,0.7259259259,0.9554455446,0.3629648239,1.0469110012054443,0.0002038851,0.4318181818,0.5031367629,0.21601103345,6.568730115890503,1.13142e-05,0.6026365348,0.660268714,0.3013239245
-qpeexact,10,13.585376024246216,0.1309477235,0.4964028777,0.9078249337,0.31367530060000004,3.6771788597106934,2.87663e-05,0.5909090909,0.3048973144,0.2954689286,88.13978791236877,0.0063847753,0.5408163265,0.8526315789,0.2736005509,10.206649780273438,0.0004451276,0.5176470588,0.954029205,0.2590460932,1.1448171138763428,0.0055893407,0.3282051282,0.533492823,0.16689723445,0.8247239589691162,0.0259349133,0.5240384615,0.7547169811,0.2749866874
-twolocalrandom,6,16.28421688079834,0.1736960552,0.2733333333,0.6559633028,0.22351469424999998,1.1685781478881836,0.112064106,0.5333333333,0.8235294118,0.32269871964999997,56.11034893989563,0.112064106,0.5333333333,0.8235294118,0.32269871964999997,3.5647048950195312,1.291e-07,0.4269005848,0.9214876033,0.21345035695,0.1778120994567871,0.025728935,0.2976190476,0.7190635452,0.16167399130000001,0.45373106002807617,0.1941960161,0.3515151515,0.6950092421,0.2728555838
-qpeexact,13,111.71675086021423,0.0102684761,0.4972972973,0.6347482725,0.2537828867,1.0817458629608154,1.169e-07,0.6689373297,0.2989493792,0.3344687233,39.87992024421692,0.0022965303,0.6031434185,0.3807785888,0.3027199744,2.7793400287628174,0.0,0.5417236662,0.8653775322,0.2708618331,1.0277960300445557,3.76416e-05,0.3820512821,0.474393531,0.19104446185,3.776853084564209,0.0001463963,0.6051948052,0.747043364,0.30267060075
-pricingput,7,96.05031323432922,0.1056789978,0.1805555556,0.5537190083,0.1431172767,5.284537076950073,0.0336161942,0.2953736655,0.870685688,0.16449492985,113.58982610702515,0.0441176155,0.2019230769,0.7234042553,0.12302034619999999,6.731935977935791,2e-10,0.2666666667,0.9236947791,0.13333333345,2.418929100036621,0.025394079,0.1666666667,0.6316831683,0.09603037284999999,0.984961986541748,0.0024342944,0.1822660099,0.7976071785,0.09235015215
-routing,2,87.34677386283875,0.9051661746,0.0,0.9333333333,0.4525830873,10.348160982131958,0.9363309001,0.0,0.9189189189,0.46816545005,108.81384706497192,0.9106288913,0.0,0.9333333333,0.45531444565,4.866328716278076,0.5695137457,0.0,0.9615384615,0.28475687285,0.15329790115356445,0.9251305142,0.0,0.8636363636,0.4625652571,0.035199880599975586,0.9160040437,0.0,0.8928571429,0.45800202185
-portfoliovqe,12,10.707543134689331,0.0002350831,0.5267349261,0.6209573092,0.26348500459999996,2.833491086959839,0.0,0.6859106529,0.4974093264,0.34295532645,65.299241065979,5.984e-07,0.7230769231,0.6048632219,0.36153876075,9.619632005691528,0.0,0.5855513308,0.9112984823,0.2927756654,3.581923246383667,6.10414e-05,0.6433021807,0.6690721649,0.32168161105,8.068521976470947,1.913e-07,0.613559322,0.6050870147,0.30677975664999996
-qgan,3,114.84312677383423,0.8797298437,0.0,0.9090909091,0.43986492185,52.70950984954834,0.4262302937,0.3555555556,0.5161290323,0.39089292465000003,5.088145971298218,0.8926267882,0.0,0.8947368421,0.4463133941,2.806859016418457,0.2034905778,0.1666666667,0.9090909091,0.18507862225,0.167647123336792,0.8924720499,0.0,0.9090909091,0.44623602495,0.1157689094543457,0.7306451782,0.0,0.88,0.3653225891
-realamprandom,4,3.573853015899658,0.4553428448,0.0,0.5833333333,0.2276714224,1.070094108581543,0.4080208817,0.2777777778,0.8363636364,0.34289932975000004,6.383778095245361,0.4080208817,0.2777777778,0.8363636364,0.34289932975000004,4.500198841094971,0.0073995049,0.1851851852,0.9262295082,0.09629234504999999,0.777083158493042,0.4924767899,0.0,0.7281553398,0.24623839495,0.21062397956848145,0.2534812171,0.0,0.8153846154,0.12674060855
-qpeinexact,4,108.58655190467834,0.7548440617,0.0,0.6557377049,0.37742203085,6.391993761062622,0.0266121674,0.1818181818,0.9243119266,0.1042151746,4.926559209823608,0.4349223929,0.0,0.9263803681,0.21746119645,11.161596059799194,0.1055332651,0.1111111111,0.9258241758,0.1083221881,0.07434797286987305,0.5601298748,0.0,0.6935483871,0.2800649374,0.1325550079345703,0.4685093669,0.0,0.8524590164,0.23425468345
-graphstate,17,9.988693952560425,0.4434202747,0.6363636364,0.7766497462,0.53989195555,14.366237163543701,0.001178223,0.7376425856,0.5385964912,0.3694104043,3.3141629695892334,0.3832663366,0.5,0.7536945813,0.4416331683,4.346958160400391,8.458e-07,0.6545454545,0.8505434783,0.32727315015,0.15298914909362793,0.2935872702,0.6842105263,0.7579617834,0.48889889825,0.4501769542694092,0.065453211,0.5194805195,0.7240143369,0.29246686525
-wstate,19,13.610251188278198,0.3086201408,0.4,0.7727272727,0.3543100704,3.484206199645996,0.1147967806,0.4086021505,0.7680798005,0.26169946555,11.027864217758179,0.2115191668,0.4166666667,0.9127272727,0.31409291675,6.278346061706543,2e-09,0.6046511628,0.9295851528,0.3023255824,1.0863699913024902,0.2556867441,0.4444444444,0.7804878049,0.35006559425,0.36614084243774414,0.1687921046,0.4444444444,0.9136690647,0.3066182745
-vqe,16,5.342388868331909,0.2953528366,0.4230769231,0.7664670659,0.35921487985,11.77484917640686,0.0008327058,0.5340501792,0.4785046729,0.2674414425,4.4085681438446045,0.4220330166,0.2857142857,0.8313253012,0.35387365115,3.861659288406372,6e-09,0.4133333333,0.9039692702,0.20666666965,0.957298994064331,0.2869059525,0.4333333333,0.8717948718,0.3601196429,0.3179359436035156,0.284808517,0.4333333333,0.9208443272,0.35907092515
-qgan,5,14.66761589050293,0.7840829344,0.0909090909,0.7380952381,0.43749601265,4.091626167297363,0.1505119289,0.2545454545,0.6764705882,0.20252869169999999,4.764292001724243,0.7777224226,0.0869565217,0.8456375839,0.43233947215,2.1697068214416504,0.514049698,0.3,0.92,0.407024849,0.07833695411682129,0.756213917,0.1,0.7752808989,0.42810695849999997,0.22158265113830566,0.5993817085,0.0909090909,0.8196721311,0.3451453997
-ghz,17,12.221067190170288,0.4327404334,0.0,0.3548387097,0.2163702167,4.14223313331604,0.056935268,0.3962264151,0.1587301587,0.22658084155,3.5584239959716797,0.4682288912,0.1621621622,0.350877193,0.3151955267,4.3969621658325195,1.0648e-06,0.3529411765,0.8617886179,0.17647112065,0.5400657653808594,0.4917645685,0.0,0.5555555556,0.24588228425,0.17719125747680664,0.3211515524,0.0,0.9238095238,0.1605757762
-qftentangled,14,15.871395826339722,0.007009293,0.6361323155,0.8914964108,0.32157080425,5.086775064468384,0.0,0.7706592386,0.235628105,0.3853296193,4.669989109039307,0.00022462,0.6456009913,0.2914837577,0.32291280565,6.156834840774536,0.0,0.4944444444,0.8642943305,0.2472222222,4.312592029571533,0.0004365852,0.5415821501,0.4570484581,0.27100936765,2.3820998668670654,5.60771e-05,0.7316017316,0.7696909272,0.36582890435
-qftentangled,3,3.195448160171509,0.7558078739,0.0,0.6315789474,0.37790393695,16.750974893569946,0.1635197867,0.1739130435,0.7472527473,0.16871641510000002,3.5569846630096436,0.8159722687,0.0,0.5882352941,0.40798613435,5.488525152206421,0.218932685,0.0,0.9254385965,0.1094663425,0.19948506355285645,0.7964175813,0.0,0.5757575758,0.39820879065,0.1355421543121338,0.7327156521,0.0,0.8658536585,0.36635782605
-graphstate,19,6.411305904388428,0.4023172844,0.5405405405,0.8370044053,0.47142891244999996,4.893460273742676,0.0007433321,0.7359050445,0.3605313093,0.3683241883,5.374675989151001,0.2776716079,0.6538461538,0.7851239669,0.46575888085,6.121644020080566,1.24e-07,0.7413793103,0.9379679144,0.37068971715000004,0.2708871364593506,0.2004650383,0.5303030303,0.671641791,0.3653840343,0.6280379295349121,0.2848446705,0.5102040816,0.8171641791,0.39752437605
-qftentangled,3,5.14868688583374,0.8262892307,0.0,0.5882352941,0.41314461535,11.871589183807373,0.2121325285,0.2,0.880952381,0.20606626425000002,90.1299741268158,0.8159722687,0.0,0.5882352941,0.40798613435,1.9145681858062744,0.4927946642,0.0,0.8780487805,0.2463973321,0.17368721961975098,0.7964175813,0.0,0.5757575758,0.39820879065,0.12990427017211914,0.7327156521,0.0,0.8658536585,0.36635782605
-qpeexact,6,5.3432457447052,0.4571372948,0.1206896552,0.5735294118,0.288913475,2.465108871459961,1.3744e-05,0.4152542373,0.9210174029,0.20763399065,95.23959183692932,0.3192585364,0.39,0.4382022472,0.3546292682,1.7361421585083008,0.0003649109,0.2631578947,0.9232323232,0.1317614028,0.16855382919311523,0.3961252675,0.1395348837,0.6532258065,0.2678300756,0.30910181999206543,0.3660436013,0.2380952381,0.7804878049,0.3020694197
-vqe,4,2.3292810916900635,0.9014360977,0.1666666667,0.9142857143,0.5340513822,3.648996114730835,0.86067454,0.1666666667,0.9142857143,0.51367060335,92.69219708442688,0.791818608,0.0,0.8101265823,0.395909304,1.1201238632202148,0.3331408308,0.1666666667,0.9558823529,0.24990374875,0.3170647621154785,0.826074105,0.1666666667,0.8928571429,0.49637038585,0.06701898574829102,0.8582481366,0.1666666667,0.925,0.51245740165
-su2random,6,8.698199033737183,0.3790764739,0.306122449,0.7954070981,0.34259946145,1.5314319133758545,0.1052334634,0.5333333333,0.8387096774,0.31928339834999997,1.6847810745239258,0.1052334634,0.5333333333,0.8387096774,0.31928339834999997,4.56505012512207,4.99e-08,0.4043715847,0.9219948849,0.20218581730000001,0.6594312191009521,0.024796075,0.2976190476,0.7307692308,0.16120756130000002,0.45980381965637207,0.1936365848,0.3515151515,0.7095070423,0.27257586815
-qpeinexact,9,104.20385479927063,0.1015385746,0.1014492754,0.697810219,0.101493925,1.0559818744659424,0.0131775379,0.4642857143,0.8523725835,0.23873162609999998,3.7792489528656006,0.0131775379,0.4642857143,0.8523725835,0.23873162609999998,5.2082319259643555,0.0017325914,0.4166666667,0.9519359146,0.20919962905,1.156515121459961,0.0482177894,0.245398773,0.5744125326,0.1468082812,1.2186577320098877,0.0957373182,0.2988505747,0.758668516,0.19729394644999998
-ghz,10,6.0898051261901855,0.6551099872,0.0,0.4642857143,0.3275549936,2.4681179523468018,0.6092643929,0.0,0.5909090909,0.30463219645,4.599642992019653,0.5939330324,0.2,0.3023255814,0.3969665162,2.592379093170166,0.3107302645,0.0,0.9727272727,0.15536513225,0.13863611221313477,0.6244589405,0.0,0.5909090909,0.31222947025,0.11356401443481445,0.748234416,0.0,0.9243697479,0.374117208
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-pricingcall,7,5.517214059829712,0.0772410928,0.1923076923,0.5881188119,0.13477439255,5.471001148223877,0.0194750975,0.3050314465,0.4960380349,0.162253272,10.909842014312744,0.0383805455,0.2652329749,0.5396039604,0.1518067602,2.3715338706970215,0.0,0.2686084142,0.9234200743,0.1343042071,1.0425360202789307,0.0288616974,0.1666666667,0.6210526316,0.09776418205,0.8174810409545898,0.0039851777,0.1567567568,0.8046462513,0.08037096725000001
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-vqe,11,3.5935637950897217,0.5077472363,0.2307692308,0.8712871287,0.36925823355,0.02746295928955078,0.3488519554,0.4,0.8611111111,0.3744259777,3.0956380367279053,0.3488519554,0.4,0.8611111111,0.3744259777,2.347277879714966,0.0888363403,0.4,0.96996997,0.24441817015,0.19760894775390625,0.3858775467,0.4,0.8742138365,0.39293877335000005,0.2273402214050293,0.6254274554,0.4,0.9212598425,0.5127137277
-vqe,8,8.529876947402954,0.7038370916,0.2,0.8648648649,0.4519185458,1.6375148296356201,0.5588673825,0.3571428571,0.9014084507,0.4580051198,3.826416015625,0.6480361177,0.2352941176,0.8595041322,0.44166511765,1.6691389083862305,0.1211950007,0.3571428571,0.9527027027,0.2391689289,0.14566564559936523,0.5831696731,0.3571428571,0.8793103448,0.47015626509999997,0.18811917304992676,0.7447973848,0.3571428571,0.9222222222,0.55097012095
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-qgan,10,4.492056131362915,0.2208907522,0.3660130719,0.6778947368,0.29345191205,2.170736789703369,0.0629651755,0.6222222222,0.9052631579,0.34259369885,2.081035852432251,0.0629651755,0.6222222222,0.9052631579,0.34259369885,10.79544186592102,0.0139551479,0.6222222222,0.9571428571,0.31808868505,1.3672528266906738,0.1348814348,0.3571428571,0.6621983914,0.24601214594999998,5.984088182449341,0.0104773174,0.5282051282,0.6441605839,0.2693412228
-qpeinexact,3,3.0650830268859863,0.8546276456,0.0,0.6666666667,0.4273138228,8.35897707939148,0.2509305807,0.15,0.8540145985,0.20046529035,3.519343137741089,0.8225537399,0.0,0.6153846154,0.41127686995,0.7979121208190918,0.6222442859,0.0,0.8615384615,0.31112214295,0.05440092086791992,0.8601125987,0.0,0.7948717949,0.43005629935,0.11440205574035645,0.6286063471,0.0,0.8529411765,0.31430317355
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-qgan,4,2.5604496002197266,0.8152097137,0.0,0.8666666667,0.40760485685,4.186537981033325,0.7350994504,0.1904761905,0.72,0.46278782045,9.788080930709839,0.7378198477,0.0555555556,0.8363636364,0.39668770165,1.3486430644989014,0.2168611761,0.0666666667,0.9448529412,0.1417639214,0.07212400436401367,0.6470855924,0.0,0.8,0.3235427962,0.15657305717468262,0.5642922007,0.0,0.8481012658,0.28214610035
-pricingput,7,4.753125905990601,1.25e-08,0.2089552239,0.9149386373,0.1044776182,19.162827968597412,0.035656456,0.2836363636,0.871914299,0.1596464098,4.561496257781982,0.0302445808,0.2578616352,0.5145038168,0.14405310799999999,7.1104960441589355,1.8e-09,0.1965811966,0.9255961844,0.09829059920000001,4.0922629833221436,0.0254020188,0.1666666667,0.6345776031,0.09603434275,0.7658572196960449,0.0024318966,0.1822660099,0.7998027613,0.09234895325
-ae,10,29.226184129714966,0.1301452715,0.35625,0.9019607843,0.24319763575,2.755006790161133,0.0074748686,0.4666666667,0.8728813559,0.23707076765,3.070812940597534,0.0074748686,0.4666666667,0.8728813559,0.23707076765,7.135077238082886,0.0,0.4311926606,0.8720657277,0.2155963303,3.8609158992767334,0.0285397361,0.3377483444,0.6621923937,0.18314404025,6.260472774505615,0.0011957619,0.4444444444,0.7361563518,0.22282010315
-qpeexact,14,11.803344964981079,0.0003692284,0.5380875203,0.3652263374,0.26922837434999997,1.4737138748168945,5e-10,0.6570545829,0.2682743029,0.3285272917,6.633636236190796,0.0171035638,0.5479452055,0.7060402685,0.28252438465,14.334007024765015,0.0,0.5501519757,0.9110089262,0.27507598785,2.881450891494751,0.0002254864,0.5169300226,0.4842840512,0.2585777545,2.3236048221588135,1.39574e-05,0.5794392523,0.7086056645,0.28972660485
-su2random,6,14.61333417892456,0.271221321,0.3092783505,0.8809815951,0.29024983575,3.0126380920410156,0.0965159183,0.5333333333,0.8557692308,0.3149246258,6.918207883834839,0.0871396605,0.5333333333,0.8717948718,0.3102364969,6.14578104019165,1.361e-07,0.4637681159,0.9206896552,0.231884126,1.5535860061645508,0.0248080512,0.2976190476,0.7263843648,0.16121354940000002,0.4802522659301758,0.1936365848,0.3515151515,0.7095070423,0.27257586815
-dj,7,12.129786968231201,0.6623994137,0.0,0.776119403,0.33119970685,48.128029346466064,0.1483718571,0.4473684211,0.3837837838,0.29787013910000004,1.9677743911743164,0.6346525088,0.0555555556,0.7428571429,0.3451040322,2.4675779342651367,0.0655235935,0.1428571429,0.9309210526,0.10419036820000001,0.036252737045288086,0.8253749198,0.0666666667,0.7321428571,0.44602079325,0.2449328899383545,0.620314123,0.0,0.8125,0.3101570615
-qpeexact,7,2.1328611373901367,0.4319944193,0.3012048193,0.5464480874,0.36659961929999996,2.6006460189819336,0.0876460519,0.4255319149,0.8544891641,0.25658898339999997,5.41917872428894,0.0876460519,0.4255319149,0.8544891641,0.25658898339999997,5.691766738891602,0.0876460519,0.4255319149,0.8544891641,0.25658898339999997,0.1775798797607422,0.3215320719,0.1891891892,0.5934065934,0.25536063055,0.4648761749267578,0.3094606472,0.3711340206,0.7550505051,0.3402973339
-realamprandom,6,40.37721800804138,0.1791150369,0.2608695652,0.5337837838,0.21999230105,0.4402201175689697,0.112064106,0.5333333333,0.8235294118,0.32269871964999997,2.3419482707977295,0.112064106,0.5333333333,0.8235294118,0.32269871964999997,1.904543161392212,1.8025e-06,0.4210526316,0.9214876033,0.21052721705,0.19913983345031738,0.0254417654,0.2976190476,0.7181208054,0.1615304065,0.4595768451690674,0.1941960161,0.3515151515,0.6950092421,0.2728555838
-qpeexact,17,27.545438051223755,9.08105e-05,0.5434782609,0.8086271075,0.2717845357,10.197752952575684,0.0,0.7219451372,0.2451764706,0.3609725686,3.599128007888794,9e-10,0.6584938704,0.3132892363,0.32924693564999996,2.279895067214966,0.0,0.5959498554,0.8667095116,0.2979749277,1.4315159320831299,1.15773e-05,0.4946949602,0.4621968616,0.24735326875,3.1012511253356934,2.9e-09,0.6194690265,0.6736462094,0.3097345147
-vqe,13,7.55754017829895,0.5484429342,0.3333333333,0.885106383,0.44088813375,4.931210994720459,0.0235552847,0.5602836879,0.5959885387,0.29191948630000003,8.209310054779053,0.5328913228,0.3333333333,0.8615384615,0.43311232805,2.5352518558502197,0.0007688802,0.2666666667,0.9282296651,0.13371777345,0.6261119842529297,0.4330592197,0.4166666667,0.875,0.4248629432,0.3011622428894043,0.5651758805,0.4166666667,0.9210526316,0.49092127360000004
-ae,5,6.988647937774658,0.5970106032,0.0,0.7575757576,0.2985053016,2.391065835952759,0.0021735187,0.1694915254,0.926980198,0.08583252205,2.605733871459961,0.5200992317,0.0681818182,0.6944444444,0.29414052495000004,2.4287149906158447,0.164878951,0.1,0.9534883721,0.13243947550000001,0.06158089637756348,0.2740158167,0.0,0.6923076923,0.13700790835,0.26797986030578613,0.3240274383,0.0571428571,0.8364485981,0.1905851477
-wstate,5,11.726270914077759,0.8052646385,0.25,0.8974358974,0.52763231925,1.2174608707427979,0.3912098493,0.25,0.968627451,0.32060492465,2.7950568199157715,0.6352503605,0.25,0.8857142857,0.44262518025,1.079807996749878,0.2590011715,0.25,0.9518072289,0.25450058575,0.1514129638671875,0.8082728053,0.25,0.7894736842,0.5291364026500001,0.11484122276306152,0.8185978349,0.25,0.9157894737,0.53429891745
-qft,9,8.337777853012085,0.1775371793,0.4357142857,0.8702502317,0.30662573249999997,0.8937151432037354,0.0136699417,0.6071428571,0.8486486486,0.31040639940000003,2.4998509883880615,0.0136699417,0.6071428571,0.8486486486,0.31040639940000003,0.6733100414276123,0.0017508473,0.5833333333,0.9518072289,0.2925420903,5.983937978744507,0.1062094793,0.3506493506,0.5536231884,0.22842941495000002,0.7772107124328613,0.0176863736,0.4402515723,0.7888446215,0.22896897295000002
-qaoa,4,5.147479772567749,0.5788589741,0.1176470588,0.6909090909,0.34825301645,7.099100112915039,0.0532326132,0.5114503817,0.6124260355,0.28234149745000003,3.1751627922058105,0.593411827,0.375,0.9166666667,0.4842059135,1.9055628776550293,0.593411827,0.375,0.9166666667,0.4842059135,0.12403011322021484,0.5664462404,0.3181818182,0.7528089888,0.4423140293,0.657573938369751,0.3969532323,0.3181818182,0.8552631579,0.35756752525
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-qpeinexact,2,2.619025945663452,0.9434194514,0.0,0.8461538462,0.4717097257,2.294070243835449,0.893710882,0.0,0.9428571429,0.446855441,2.564268112182617,0.9592822771,0.0,0.8461538462,0.47964113855,2.1668918132781982,0.7696829522,0.0,0.9428571429,0.3848414761,0.017694950103759766,0.9522376508,0.0,0.8333333333,0.4761188254,0.13348913192749023,0.9675110774,0.0,0.9230769231,0.4837555387
-ae,12,18.638953924179077,0.0190151989,0.4579710145,0.6796657382,0.23849310669999998,5.090399742126465,4.121e-06,0.5858585859,0.4118811881,0.29293135345,2.6860239505767822,0.0023756047,0.5573476703,0.4051172708,0.2798616375,0.46907973289489746,0.0,0.4052287582,0.878667724,0.2026143791,0.3176109790802002,0.021673062,0.3333333333,0.605015674,0.17750319765,4.247284412384033,0.0060521036,0.4021164021,0.7254901961,0.20408425285
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-qpeexact,6,5.854932069778442,0.4590703288,0.1272727273,0.5864661654,0.29317152805,6.166842222213745,0.3395434643,0.3711340206,0.4457142857,0.35533874244999997,5.556429862976074,0.3395434643,0.3711340206,0.4457142857,0.35533874244999997,1.929210901260376,0.0052953505,0.1857142857,0.9237472767,0.0955048181,0.0918428897857666,0.5206335674,0.1333333333,0.6456692913,0.32698345035,0.4446561336517334,0.3659980938,0.2380952381,0.7741935484,0.30204666594999996
-routing,12,4.507284879684448,0.4280809383,0.537037037,0.8235294118,0.48255898765000005,11.093540906906128,0.0063845449,0.5901639344,0.5793103448,0.29827423965,3.231253147125244,0.4820894872,0.3333333333,0.84,0.40771141025,2.6304590702056885,0.0,0.5454545455,0.9707446809,0.27272727275,0.2852468490600586,0.3612534558,0.5454545455,0.8607594937,0.45335400065000003,0.3316769599914551,0.5497215808,0.5454545455,0.915601023,0.5475880631500001
-realamprandom,7,17.925944089889526,0.1275167746,0.3384615385,0.8545861298,0.23298915655,1.5834879875183105,0.0475575351,0.6031746032,0.82,0.32536606915000005,1.8637888431549072,0.0475575351,0.6031746032,0.82,0.32536606915000005,11.08240032196045,0.0,0.501650165,0.9199048374,0.2508250825,2.3153581619262695,0.2486460306,0.3666666667,0.6314496314,0.30765634865,0.6806187629699707,0.0058942789,0.4480874317,0.7288888889,0.2269908553
-ae,11,18.076337337493896,0.0771623955,0.3970588235,0.866207575,0.23711060949999999,1.4292211532592773,0.0026144645,0.5090909091,0.8707403055,0.2558526868,6.462707042694092,0.0010960618,0.4285714286,0.6944660951,0.2148337452,6.259806871414185,5.13264e-05,0.5090909091,0.9532113994,0.25457111775,0.4208509922027588,0.0519404228,0.358974359,0.6161417323,0.2054573909,10.776274919509888,0.0020354999,0.4576923077,0.7565543071,0.22986390380000002
-qpeexact,15,12.99933123588562,0.000275325,0.483713355,0.6594564615,0.24199434,1.1621901988983154,0.0,0.6491994178,0.2315436242,0.3245997089,2.8058202266693115,0.0006981018,0.5749636099,0.3760217984,0.28783085584999996,4.182435035705566,0.0,0.5833333333,0.8563474388,0.29166666665,1.1474082469940186,0.0001504448,0.4877589454,0.4947668887,0.24395469509999998,7.329416036605835,1.10341e-05,0.535483871,0.7117619712,0.26774745255
-su2random,7,7.8984010219573975,0.013282227,0.34375,0.2344497608,0.1785161135,1.7685530185699463,0.0354617551,0.6031746032,0.8636363636,0.31931817915000005,1.8235390186309814,0.0354617551,0.6031746032,0.8636363636,0.31931817915000005,10.942023992538452,0.0,0.5218855219,0.9199676637,0.26094276095,2.5288162231445312,0.2485170235,0.3666666667,0.6411483254,0.3075918451,0.670600175857544,0.0058574701,0.4480874317,0.7389443652,0.22697245089999998
-graphstate,6,4.312006950378418,0.7598399082,0.3888888889,0.7692307692,0.57436439855,2.813817024230957,0.5668216762,0.3571428571,0.5882352941,0.46198226665,4.121548175811768,0.6397588564,0.3703703704,0.6666666667,0.5050646134,12.175984859466553,0.2210866983,0.3333333333,0.9411764706,0.2772100158,0.18398284912109375,0.6883333052,0.375,0.75,0.5316666526,0.17830872535705566,0.5118670778,0.25,0.7391304348,0.3809335389
-qgan,5,4.106158018112183,0.6554390243,0.1785714286,0.7971014493,0.41700522645,1.8236212730407715,0.5276979246,0.3,0.9130434783,0.4138489623,1.8947010040283203,0.5276979246,0.3,0.9130434783,0.4138489623,9.830071210861206,0.5276979246,0.3,0.9130434783,0.4138489623,0.29622483253479004,0.7561991086,0.1,0.7752808989,0.42809955429999996,0.22644519805908203,0.5993817085,0.0909090909,0.8196721311,0.3451453997
-realamprandom,2,1.6300699710845947,0.9576746461,0.0,0.9333333333,0.47883732305,3.554547071456909,0.8463049475,0.0,0.88,0.42315247375,10.657647132873535,0.8775136943,0.0,0.9333333333,0.43875684715,0.04167008399963379,0.5695137457,0.0,0.9615384615,0.28475687285,0.13580894470214844,0.9247365667,0.0,0.8636363636,0.46236828335,0.06786108016967773,0.9160040437,0.0,0.8928571429,0.45800202185
-vqe,9,2.9185948371887207,0.7062004645,0.2631578947,0.8582089552,0.48467917959999995,3.516892910003662,0.5223942782,0.375,0.9304347826,0.4486971391,4.734737873077393,0.6853188429,0.2631578947,0.8582089552,0.47423836880000003,1.4372329711914062,0.4297367619,0.375,0.8620689655,0.40236838095,0.2954549789428711,0.59040489,0.375,0.875,0.482702445,0.17362689971923828,0.6983255421,0.375,0.921182266,0.53666277105
-qgan,11,2.8440799713134766,0.1426888693,0.4759358289,0.7068965517,0.3093123491,2.327216148376465,0.0348313498,0.6545454545,0.9038461538,0.34468840215,6.090322971343994,0.0348313498,0.6545454545,0.9038461538,0.34468840215,1.973351001739502,0.005651699,0.6545454545,0.9565560821,0.33009857675,1.5663156509399414,0.0269610578,0.3630136986,0.73012939,0.1949873782,10.40052604675293,0.0067150485,0.5707317073,0.6740858506,0.2887233779
-qpeinexact,2,3.338870048522949,0.96530833,0.0,0.8461538462,0.482654165,3.682309865951538,0.893710882,0.0,0.9428571429,0.446855441,1.5516879558563232,0.9645415489,0.0,0.8461538462,0.48227077445,1.2422912120819092,0.7516399484,0.0,0.9428571429,0.3758199742,0.03059673309326172,0.9568938315,0.0,0.8333333333,0.47844691575,0.032022953033447266,0.9675110774,0.0,0.9230769231,0.4837555387
-graphstate,11,4.406788110733032,0.5892257183,0.5769230769,0.8088235294,0.5830743976,1.9361488819122314,0.0141297234,0.7125748503,0.3971119134,0.36335228685,4.131304025650024,0.4879381463,0.5454545455,0.9166666667,0.5166963459,0.30359697341918945,0.4879381463,0.5454545455,0.9166666667,0.5166963459,0.25755810737609863,0.5218355092,0.4230769231,0.7614678899,0.47245621615,0.301131010055542,0.2853316752,0.6037735849,0.7087912088,0.44455263005
-vqe,10,2.6999399662017822,0.6502629146,0.3888888889,0.8988764045,0.51957590175,2.148242950439453,0.0666437171,0.5225225225,0.5904059041,0.29458311979999996,1.9693717956542969,0.6762583973,0.2857142857,0.8609271523,0.4809863415,2.5941669940948486,0.1129922811,0.3888888889,0.97,0.250940585,0.13040685653686523,0.4528880164,0.3888888889,0.8767123288,0.42088845264999997,0.1939408779144287,0.6696766899,0.3888888889,0.9217391304,0.5292827894
-qgan,3,2.541908025741577,0.7991305185,0.0,0.8947368421,0.39956525925,0.3871440887451172,0.1266127263,0.4736842105,0.5928571429,0.3001484684,1.2980079650878906,0.7968053488,0.0,0.9375,0.3984026744,3.0502939224243164,0.3643190025,0.0,0.9512195122,0.18215950125,0.2089838981628418,0.8924720499,0.0,0.9069767442,0.44623602495,0.11440277099609375,0.7306451782,0.0,0.88,0.3653225891
-ghz,11,6.193110942840576,0.6134744676,0.0,0.4242424242,0.3067372338,0.14685797691345215,0.1551288244,0.4285714286,0.1860465116,0.29185012650000003,4.013740062713623,0.3860936684,0.2884615385,0.8586956522,0.33727760345,2.03548002243042,0.2733634285,0.0,0.9726775956,0.13668171425,0.08275985717773438,0.5843607153,0.0,0.5833333333,0.29218035765,0.11338686943054199,0.7114299644,0.0,0.9242424242,0.3557149822
-qgan,10,7.965870141983032,0.2519159207,0.4420289855,0.5519480519,0.3469724531,0.9638140201568604,0.0663530628,0.6222222222,0.9010989011,0.3442876425,2.4453558921813965,0.0663530628,0.6222222222,0.9010989011,0.3442876425,2.0631330013275146,0.0139551479,0.6222222222,0.9571428571,0.31808868505,0.5964579582214355,0.1348365983,0.3571428571,0.6675461741,0.2459897277,5.9167320728302,0.0104773174,0.5282051282,0.6441605839,0.2693412228
-qftentangled,12,6.0051538944244385,0.0281413443,0.6838046272,0.3940809969,0.35597298575,1.807046890258789,2.41e-07,0.7409793814,0.2429268293,0.37048981119999996,3.0359013080596924,0.000280193,0.6356164384,0.3834459459,0.31794831570000004,2.644613265991211,0.0,0.5026737968,0.8695500523,0.2513368984,0.5654418468475342,0.0010169979,0.496969697,0.5161290323,0.24899334744999999,7.45321798324585,0.0022560302,0.5331491713,0.7601060305,0.26770260074999996
-qftentangled,5,6.157814025878906,0.564487323,0.1527777778,0.424,0.3586325504,1.9720849990844727,0.2165630057,0.3666666667,0.847715736,0.2916148362,2.9590392112731934,0.4293512357,0.1481481481,0.4545454545,0.2887496919,2.3074660301208496,0.008329665,0.3333333333,0.9223803364,0.17083149914999998,0.06559896469116211,0.5712720422,0.1224489796,0.5148514851,0.34686051090000003,0.3028852939605713,0.5823518822,0.1282051282,0.84765625,0.3552785052
-qftentangled,5,1.678779125213623,0.3570999942,0.2222222222,0.3706293706,0.2896611082,2.3068668842315674,0.2165630057,0.3666666667,0.847715736,0.2916148362,2.7595279216766357,0.6249462765,0.1481481481,0.4545454545,0.3865472123,0.8140838146209717,7.4145e-06,0.3771929825,0.9197748065,0.1886001985,0.07128310203552246,0.5712720422,0.1224489796,0.5148514851,0.34686051090000003,0.31345582008361816,0.5823518822,0.1282051282,0.84765625,0.3552785052
-realamprandom,11,3.4535129070281982,1.895e-07,0.5634278003,0.1348511384,0.2817139949,2.0790021419525146,0.000377835,0.7515151515,0.8125,0.37594649325,3.828450918197632,0.000377835,0.7515151515,0.8125,0.37594649325,2.9245059490203857,2.551e-07,0.7515151515,0.9549918167,0.3757577033,1.1861040592193604,0.0060751509,0.564738292,0.7148468185,0.28540672145,11.565106868743896,8.219e-07,0.6319758673,0.6431646932,0.31598834459999997
-ghz,16,3.152751922607422,0.4156815203,0.4285714286,0.3114754098,0.42212647445,2.028439998626709,0.0089966957,0.4528301887,0.106741573,0.2309134422,2.724282741546631,0.3590379868,0.3148148148,0.2602739726,0.3369264008,3.3412578105926514,1e-09,0.4888888889,0.9182561308,0.24444444495,0.34786224365234375,0.5000809639,0.0,0.5588235294,0.25004048195,0.16561293601989746,0.3807657009,0.0,0.923857868,0.19038285045
-tsp,9,2.5057528018951416,0.5085272193,0.6,0.9284436494,0.55426360965,1.619553804397583,0.3243657171,0.6,0.9284436494,0.46218285854999996,2.486893892288208,0.5243688455,0.6,0.9284436494,0.5621844227499999,1.8307340145111084,0.085437508,0.6,0.9054373522,0.34271875399999996,0.2758920192718506,0.2970586081,0.6,0.8795180723,0.44852930405,0.40184712409973145,0.529253429,0.6,0.9175257732,0.5646267145
diff --git a/src/mqt/predictor/rl/training_data/training_circuits/mqtbench_sample_circuits.zip b/src/mqt/predictor/rl/training_data/training_circuits/mqtbench_sample_circuits.zip
deleted file mode 100644
index c816eabf0..000000000
Binary files a/src/mqt/predictor/rl/training_data/training_circuits/mqtbench_sample_circuits.zip and /dev/null differ
diff --git a/src/mqt/predictor/rl/training_data/training_circuits/training_data_compilation.zip b/src/mqt/predictor/rl/training_data/training_circuits/training_data_compilation.zip
new file mode 100644
index 000000000..2e51e3333
Binary files /dev/null and b/src/mqt/predictor/rl/training_data/training_circuits/training_data_compilation.zip differ
diff --git a/src/mqt/predictor/utils.py b/src/mqt/predictor/utils.py
index 26f83a4bd..5d09587c4 100644
--- a/src/mqt/predictor/utils.py
+++ b/src/mqt/predictor/utils.py
@@ -4,7 +4,7 @@
import signal
from typing import Any
-logger = logging.getLogger("mqtpredictor")
+logger = logging.getLogger("mqt-predictor")
def timeout_watcher(func: Any, args: list[Any], timeout: int) -> Any:
@@ -39,6 +39,7 @@ def timeout_handler(_signum: Any, _frame: Any) -> None: # Custom signal handler
def get_rigetti_qubit_dict() -> dict[str, str]:
+ """Returns a dictionary mapping the qubit indices of the Rigetti Aspen-M2 device to a sequence of numbers that does not skip index numbers."""
mapping = {
"32": "4",
"39": "3",
diff --git a/tests/compilation/test_helper_rl.py b/tests/compilation/test_helper_rl.py
new file mode 100644
index 000000000..f3896ff8c
--- /dev/null
+++ b/tests/compilation/test_helper_rl.py
@@ -0,0 +1,60 @@
+from pathlib import Path
+
+import pytest
+from mqt.bench import get_benchmark
+from mqt.predictor import rl
+
+
+def test_get_actions_opt() -> None:
+ assert len(rl.helper.get_actions_opt()) == rl.helper.NUM_ACTIONS_OPT
+
+
+def test_get_actions_layout() -> None:
+ assert len(rl.helper.get_actions_layout()) == rl.helper.NUM_ACTIONS_LAYOUT
+
+
+def test_et_actions_routing() -> None:
+ assert len(rl.helper.get_actions_routing()) == rl.helper.NUM_ACTIONS_ROUTING
+
+
+def test_get_actions_synthesis() -> None:
+ assert len(rl.helper.get_actions_synthesis()) == rl.helper.NUM_ACTIONS_SYNTHESIS
+
+
+def test_get_action_terminate() -> None:
+ assert len(rl.helper.get_action_terminate()) == rl.helper.NUM_ACTIONS_TERMINATE
+
+
+def test_get_actions_devices() -> None:
+ assert len(rl.helper.get_devices()) == rl.helper.NUM_ACTIONS_DEVICES
+
+
+def test_get_actions_mapping() -> None:
+ assert len(rl.helper.get_actions_mapping()) == rl.helper.NUM_ACTIONS_MAPPING
+
+
+@pytest.mark.parametrize(
+ "device",
+ ["ibm_washington", "ibm_montreal", "rigetti_aspen_m2", "oqc_lucy", "ionq_harmony", "ionq_aria1", "quantinuum_h2"],
+)
+def test_get_device(device: str) -> None:
+ assert rl.helper.get_device(device)
+
+
+def test_create_feature_dict() -> None:
+ qc = get_benchmark("dj", 1, 5)
+ features = rl.helper.create_feature_dict(qc)
+ assert features
+ assert len(features) == rl.helper.NUM_FEATURE_VECTOR_ELEMENTS
+
+
+def test_get_path_trained_model() -> None:
+ path = rl.helper.get_path_trained_model()
+ assert path.exists()
+ assert isinstance(path, Path)
+
+
+def test_get_path_training_circuits() -> None:
+ path = rl.helper.get_path_training_circuits()
+ assert path.exists()
+ assert isinstance(path, Path)
diff --git a/tests/compilation/test_predictor_rl.py b/tests/compilation/test_predictor_rl.py
new file mode 100644
index 000000000..0035aa393
--- /dev/null
+++ b/tests/compilation/test_predictor_rl.py
@@ -0,0 +1,43 @@
+import os
+
+import pytest
+from mqt.bench import get_benchmark
+from mqt.predictor import reward, rl
+from qiskit import QuantumCircuit
+
+IN_GITHUB_ACTIONS = os.getenv("GITHUB_ACTIONS") == "true"
+
+
+@pytest.mark.skipif(
+ IN_GITHUB_ACTIONS,
+ reason="Trained models are not yet available. Skipping may be reverted after release with trained models.",
+)
+@pytest.mark.parametrize(
+ "figure_of_merit",
+ ["expected_fidelity", "critical_depth"],
+)
+def test_qcompile_with_pretrained_models(figure_of_merit: reward.figure_of_merit) -> None:
+ qc = get_benchmark("ghz", 1, 3)
+ qc_compiled, compilation_information = rl.qcompile(qc, figure_of_merit=figure_of_merit, device_name="ionq_harmony")
+ assert isinstance(qc_compiled, QuantumCircuit)
+ assert compilation_information is not None
+
+
+@pytest.mark.parametrize(
+ "figure_of_merit",
+ ["expected_fidelity", "critical_depth"],
+)
+def test_qcompile_with_newly_trained_models(figure_of_merit: reward.figure_of_merit) -> None:
+ predictor = rl.Predictor(figure_of_merit=figure_of_merit, device_name="ionq_harmony")
+ predictor.train_model(
+ timesteps=100,
+ test=True,
+ )
+
+ qc = get_benchmark("ghz", 1, 5)
+ res = rl.qcompile(qc, figure_of_merit=figure_of_merit, device_name="ionq_harmony")
+ assert type(res) == tuple
+ qc_compiled, compilation_information = res
+
+ assert isinstance(qc_compiled, QuantumCircuit)
+ assert compilation_information is not None
diff --git a/tests/device_selection/test_helper_ml.py b/tests/device_selection/test_helper_ml.py
new file mode 100644
index 000000000..6ff911b34
--- /dev/null
+++ b/tests/device_selection/test_helper_ml.py
@@ -0,0 +1,58 @@
+from mqt.bench import benchmark_generator
+from mqt.predictor import ml
+
+
+def test_get_index_to_device_LUT() -> None:
+ expected = {
+ 0: "ibm_washington",
+ 1: "ibm_montreal",
+ 2: "oqc_lucy",
+ 3: "rigetti_aspen_m2",
+ 4: "ionq_harmony",
+ 5: "ionq_aria1",
+ 6: "quantinuum_h2",
+ }
+ assert ml.helper.get_index_to_device_LUT() == expected
+
+
+def test_load_training_data() -> None:
+ assert ml.helper.load_training_data() is not None
+
+
+def test_create_feature_dict() -> None:
+ qc = benchmark_generator.get_benchmark("dj", 1, 3)
+ feature_vector = ml.helper.create_feature_dict(qc)
+ assert feature_vector is not None
+
+
+def test_get_openqasm_gates() -> None:
+ assert ml.helper.get_openqasm_gates() is not None
+
+
+def test_get_path_training_circuits() -> None:
+ path = ml.helper.get_path_training_circuits()
+ assert path.exists()
+
+
+def test_get_path_training_circuits_compiled() -> None:
+ path = ml.helper.get_path_training_circuits_compiled()
+ assert path.exists()
+
+
+def test_get_path_training_data() -> None:
+ path = ml.helper.get_path_training_data()
+ assert path.exists()
+
+
+def test_get_path_trained_model() -> None:
+ for figure_of_merit in ["expected_fidelity", "critical_depth"]:
+ path = ml.helper.get_path_trained_model(figure_of_merit=figure_of_merit)
+ assert path.exists()
+
+
+def test_predict_device_for_figure_of_merit() -> None:
+ qc = benchmark_generator.get_benchmark("ghz", 1, 5)
+ assert (
+ ml.helper.predict_device_for_figure_of_merit(qc, "expected_fidelity")
+ in ml.helper.get_index_to_device_LUT().values()
+ )
diff --git a/tests/device_selection/test_predictor_ml.py b/tests/device_selection/test_predictor_ml.py
new file mode 100644
index 000000000..764846718
--- /dev/null
+++ b/tests/device_selection/test_predictor_ml.py
@@ -0,0 +1,81 @@
+from pathlib import Path
+from typing import Final
+
+import numpy as np
+from mqt.bench import benchmark_generator
+from mqt.predictor import ml, reward
+
+
+def test_predict() -> None:
+ path = ml.helper.get_path_trained_model(figure_of_merit="expected_fidelity")
+ assert path.is_file()
+ filename = "test_qasm.qasm"
+ figure_of_merit: Final = "expected_fidelity"
+ qc = benchmark_generator.get_benchmark("dj", 1, 8)
+ qc.qasm(filename=filename)
+ predictor = ml.Predictor()
+ predictions = predictor.predict_probs(filename, figure_of_merit=figure_of_merit)
+ assert predictor.clf is not None
+ classes = predictor.clf.classes_ # type: ignore[unreachable]
+ predicted_device_indices = classes[np.argsort(predictions)[::-1]]
+ assert all(0 <= i < len(ml.helper.get_index_to_device_LUT()) for i in predicted_device_indices)
+ predictions = predictor.predict_probs(qc.qasm(), figure_of_merit=figure_of_merit)
+ predicted_device_indices = classes[np.argsort(predictions)[::-1]]
+ assert all(0 <= i < len(ml.helper.get_index_to_device_LUT()) for i in predicted_device_indices)
+ Path(filename).unlink()
+
+
+def test_train_random_forest_classifier() -> None:
+ predictor = ml.Predictor()
+ assert predictor.clf is None
+ predictor.train_random_forest_classifier(visualize_results=False)
+
+ assert predictor.clf is not None
+
+
+def test_compile_all_circuits_for_dev_and_fom() -> None:
+ predictor = ml.Predictor()
+ source_path = Path()
+ target_path = Path("test_compiled_circuits")
+ if not target_path.exists():
+ target_path.mkdir()
+ figure_of_merit: reward.figure_of_merit = "expected_fidelity"
+
+ qc = benchmark_generator.get_benchmark("dj", 1, 3)
+ qasm_path = Path("test.qasm")
+ qc.qasm(filename=str(qasm_path))
+ predictor.compile_all_circuits_devicewise(
+ device_name="ionq_harmony",
+ timeout=100,
+ figure_of_merit=figure_of_merit,
+ source_path=source_path,
+ target_path=target_path,
+ )
+ assert any(file.suffix == ".qasm" for file in target_path.iterdir())
+
+ training_sample, circuit_name, scores = predictor.generate_training_sample(
+ file=qasm_path,
+ figure_of_merit=figure_of_merit,
+ path_uncompiled_circuit=source_path,
+ path_compiled_circuits=target_path,
+ )
+ assert training_sample
+ assert circuit_name is not None
+ assert any(score != -1 for score in scores)
+
+ (
+ training_data,
+ name_list,
+ scores_list,
+ ) = predictor.generate_trainingdata_from_qasm_files(figure_of_merit, source_path, target_path)
+ assert len(training_data) > 0
+ assert len(name_list) > 0
+ assert len(scores_list) > 0
+
+ if target_path.exists():
+ for file in target_path.iterdir():
+ file.unlink()
+ target_path.rmdir()
+
+ if qasm_path.exists():
+ qasm_path.unlink()
diff --git a/tests/ml/test_helper_ml.py b/tests/ml/test_helper_ml.py
deleted file mode 100644
index 82ec72330..000000000
--- a/tests/ml/test_helper_ml.py
+++ /dev/null
@@ -1,93 +0,0 @@
-from pathlib import Path
-
-from mqt.bench import benchmark_generator, qiskit_helper
-from mqt.predictor import ml, reward
-
-
-def test_get_width_penalty() -> None:
- assert ml.helper.get_width_penalty() < 0
-
-
-def test_get_index_to_comppath_LUT() -> None:
- expected = {
- 0: ("ibm", "ibm_washington", "qiskit", 0),
- 1: ("ibm", "ibm_washington", "qiskit", 1),
- 2: ("ibm", "ibm_washington", "qiskit", 2),
- 3: ("ibm", "ibm_washington", "qiskit", 3),
- 4: ("ibm", "ibm_washington", "tket", False),
- 5: ("ibm", "ibm_washington", "tket", True),
- 6: ("ibm", "ibm_montreal", "qiskit", 0),
- 7: ("ibm", "ibm_montreal", "qiskit", 1),
- 8: ("ibm", "ibm_montreal", "qiskit", 2),
- 9: ("ibm", "ibm_montreal", "qiskit", 3),
- 10: ("ibm", "ibm_montreal", "tket", False),
- 11: ("ibm", "ibm_montreal", "tket", True),
- 12: ("rigetti", "rigetti_aspen_m2", "qiskit", 0),
- 13: ("rigetti", "rigetti_aspen_m2", "qiskit", 1),
- 14: ("rigetti", "rigetti_aspen_m2", "qiskit", 2),
- 15: ("rigetti", "rigetti_aspen_m2", "qiskit", 3),
- 16: ("rigetti", "rigetti_aspen_m2", "tket", False),
- 17: ("rigetti", "rigetti_aspen_m2", "tket", True),
- 18: ("ionq", "ionq11", "qiskit", 0),
- 19: ("ionq", "ionq11", "qiskit", 1),
- 20: ("ionq", "ionq11", "qiskit", 2),
- 21: ("ionq", "ionq11", "qiskit", 3),
- 22: ("ionq", "ionq11", "tket", False),
- 23: ("ionq", "ionq11", "tket", True),
- 24: ("oqc", "oqc_lucy", "qiskit", 0),
- 25: ("oqc", "oqc_lucy", "qiskit", 1),
- 26: ("oqc", "oqc_lucy", "qiskit", 2),
- 27: ("oqc", "oqc_lucy", "qiskit", 3),
- 28: ("oqc", "oqc_lucy", "tket", False),
- 29: ("oqc", "oqc_lucy", "tket", True),
- }
- assert ml.helper.get_index_to_comppath_LUT() == expected
-
-
-def test_get_compilation_pipeline() -> None:
- expected = {
- "devices": {
- "ibm": [("ibm_washington", 127), ("ibm_montreal", 27)],
- "rigetti": [("rigetti_aspen_m2", 80)],
- "ionq": [("ionq11", 11)],
- "oqc": [("oqc_lucy", 8)],
- },
- "compiler": {
- "qiskit": {"optimization_level": [0, 1, 2, 3]},
- "tket": {"lineplacement": [False, True]},
- },
- }
- assert ml.helper.get_compilation_pipeline() == expected
-
-
-def test_load_training_data() -> None:
- assert ml.helper.load_training_data() is not None
-
-
-def test_calc_eval_score_for_qc() -> None:
- qc = benchmark_generator.get_benchmark("dj", 1, 3)
- compilation_pipeline = ml.helper.get_compilation_pipeline()
-
- filename_qasm = "eval_test.qasm"
- for gate_set_name, devices in compilation_pipeline["devices"].items():
- for device_name, max_qubits in devices:
- for compiler, settings in compilation_pipeline["compiler"].items():
- if "qiskit" in compiler:
- for opt_level in settings["optimization_level"]:
- if max_qubits >= qc.num_qubits:
- qiskit_helper.get_mapped_level(
- qc,
- gate_set_name,
- qc.num_qubits,
- device_name,
- opt_level,
- False,
- False,
- ".",
- "eval_test",
- )
- score = reward.expected_fidelity(filename_qasm, device=device_name)
- assert score >= 0 and score <= 1 or score == ml.helper.get_width_penalty()
-
- if Path(filename_qasm).is_file():
- Path(filename_qasm).unlink()
diff --git a/tests/ml/test_predictor_ml.py b/tests/ml/test_predictor_ml.py
deleted file mode 100644
index ed170b387..000000000
--- a/tests/ml/test_predictor_ml.py
+++ /dev/null
@@ -1,112 +0,0 @@
-from pathlib import Path
-from typing import Any
-from unittest.mock import patch
-
-import pytest
-from mqt.bench import benchmark_generator
-from mqt.predictor import ml
-
-
-@patch("matplotlib.pyplot.show")
-def test_predict(mock_show: Any) -> None: # noqa: ARG001
- path = ml.helper.get_path_trained_model() / "trained_clf.joblib"
- assert path.is_file()
- filename = "test_qasm.qasm"
- qc = benchmark_generator.get_benchmark("dj", 1, 8)
- qc.qasm(filename=filename)
- predictor = ml.Predictor()
- prediction = predictor.predict(filename)
- assert 0 <= prediction < len(ml.helper.get_index_to_comppath_LUT())
- prediction = predictor.predict(qc.qasm())
- assert 0 <= prediction < len(ml.helper.get_index_to_comppath_LUT())
- with pytest.raises(ValueError, match="Invalid input for 'qc' parameter"):
- predictor.predict("Error Test")
-
- predictor.clf = None
- prediction = predictor.predict(filename)
- Path(filename).unlink()
- assert 0 <= prediction < len(ml.helper.get_index_to_comppath_LUT())
-
-
-@pytest.mark.parametrize("comp_path", list(range(len(ml.helper.get_index_to_comppath_LUT()))))
-def test_compilation_paths(comp_path: int) -> None: # noqa: ARG001
- qc_qasm = benchmark_generator.get_benchmark("dj", 1, 2).qasm()
- res, compile_info = ml.qcompile(qc_qasm)
- assert res
- assert compile_info
-
- qc = benchmark_generator.get_benchmark("dj", 1, 2)
- tmp_filename = "test.qasm"
- qc.qasm(filename=tmp_filename)
- res, compile_info = ml.qcompile(tmp_filename)
- assert res
- assert compile_info
-
- if Path(tmp_filename).exists():
- Path(tmp_filename).unlink()
-
-
-def test_compile_all_circuits_for_qc() -> None:
- qc = benchmark_generator.get_benchmark("dj", 1, 2)
- tmp_filename = "test.qasm"
- qc.qasm(filename=tmp_filename)
- predictor = ml.Predictor()
- assert predictor.compile_all_circuits_for_qc(
- filename=tmp_filename,
- source_path=".",
- )
- if Path(tmp_filename).exists():
- Path(tmp_filename).unlink()
-
-
-@patch("matplotlib.pyplot.show")
-def test_train_random_forest_classifier(mock_pyplot: Any) -> None: # noqa: ARG001
- predictor = ml.Predictor()
- assert predictor.clf is None
- predictor.train_random_forest_classifier(visualize_results=False)
- if Path("non_zero_indices.npy").exists():
- Path("non_zero_indices.npy").unlink()
-
- assert predictor.clf is not None
-
-
-def test_generate_compiled_circuits() -> None:
- predictor = ml.Predictor()
- source_path = "."
- target_path = Path("test_compiled_circuits")
- if not target_path.exists():
- target_path.mkdir()
-
- qc = benchmark_generator.get_benchmark("dj", 1, 3)
- qasm_path = Path("compiled_test.qasm")
- qc.qasm(filename=str(qasm_path))
- predictor.generate_compiled_circuits(source_path, str(target_path))
- assert any(file.suffix == ".qasm" for file in target_path.iterdir())
-
- res = predictor.generate_training_sample(
- str(qasm_path),
- path_uncompiled_circuit=source_path,
- path_compiled_circuits=str(target_path),
- )
- assert not isinstance(res, bool)
- training_sample, circuit_name, scores = res
- assert training_sample
- assert circuit_name
- assert scores
-
- (
- training_data,
- name_list,
- scores_list,
- ) = predictor.generate_trainingdata_from_qasm_files(source_path, str(target_path))
- assert training_data
- assert name_list
- assert scores_list
-
- if target_path.exists():
- for file in target_path.iterdir():
- file.unlink()
- target_path.rmdir()
-
- if qasm_path.exists():
- qasm_path.unlink()
diff --git a/tests/rl/test_helper_rl.py b/tests/rl/test_helper_rl.py
deleted file mode 100644
index 23349ed14..000000000
--- a/tests/rl/test_helper_rl.py
+++ /dev/null
@@ -1,127 +0,0 @@
-from pathlib import Path
-
-import pytest
-from mqt.bench import get_benchmark
-from mqt.predictor import rl
-from qiskit import QuantumCircuit
-
-NUM_ACTIONS_OPT = 12
-
-
-def test_get_actions_opt() -> None:
- assert len(rl.helper.get_actions_opt()) == NUM_ACTIONS_OPT
-
-
-NUM_ACTIONS_LAYOUT = 3
-
-
-def test_get_actions_layout() -> None:
- assert len(rl.helper.get_actions_layout()) == NUM_ACTIONS_LAYOUT
-
-
-NUM_ACTIONS_ROUTING = 4
-
-
-def test_et_actions_routing() -> None:
- assert len(rl.helper.get_actions_routing()) == NUM_ACTIONS_ROUTING
-
-
-NUM_ACTIONS_PLATFORM = 4
-
-
-def test_get_actions_platform_selection() -> None:
- assert len(rl.helper.get_actions_platform_selection()) == NUM_ACTIONS_PLATFORM
-
-
-NUM_ACTIONS_SYNTHESIS = 1
-
-
-def test_get_actions_synthesis() -> None:
- assert len(rl.helper.get_actions_synthesis()) == NUM_ACTIONS_SYNTHESIS
-
-
-NUM_ACTIONS_TERMINATE = 1
-
-
-def test_get_action_terminate() -> None:
- assert len(rl.helper.get_action_terminate()) == NUM_ACTIONS_TERMINATE
-
-
-NUM_ACTIONS_DEVICES = 5
-
-
-def test_get_actions_devices() -> None:
- assert len(rl.helper.get_actions_devices()) == NUM_ACTIONS_DEVICES
-
-
-def test_get_random_state_sample() -> None:
- sample = rl.helper.get_state_sample()
- assert sample
- assert isinstance(sample, QuantumCircuit)
-
-
-def test_get_ibm_native_gates() -> None:
- assert rl.helper.get_ibm_native_gates() == ["rz", "sx", "x", "cx", "measure"]
-
-
-def test_get_rigetti_native_gates() -> None:
- assert rl.helper.get_rigetti_native_gates() == ["rx", "rz", "cz", "measure"]
-
-
-def test_get_ionq_native_gates() -> None:
- assert rl.helper.get_ionq_native_gates() == ["rxx", "rz", "ry", "rx", "measure"]
-
-
-def test_get_oqc_native_gates() -> None:
- assert rl.helper.get_oqc_native_gates() == ["rz", "sx", "x", "ecr", "measure"]
-
-
-NUM_CONNECTIONS_RIGETTI_M2 = 212
-
-
-def test_get_rigetti_aspen_m2_map() -> None:
- assert len(rl.helper.get_rigetti_aspen_m2_map()) == NUM_CONNECTIONS_RIGETTI_M2
-
-
-NUM_CONNECTIONS_IONQ_11 = 110
-
-
-def test_get_ionq11_c_map() -> None:
- assert len(rl.helper.get_ionq11_c_map()) == NUM_CONNECTIONS_IONQ_11
-
-
-NUM_CONNECTIONS_OQC_LUCY = 8
-
-
-def test_get_cmap_oqc_lucy() -> None:
- assert len(rl.helper.get_cmap_oqc_lucy()) == NUM_CONNECTIONS_OQC_LUCY
-
-
-@pytest.mark.parametrize(
- "device",
- ["ibm_washington", "ibm_montreal", "rigetti_aspen_m2", "oqc_lucy", "ionq11"],
-)
-def test_get_cmap_from_devicename(device: str) -> None:
- assert rl.helper.get_cmap_from_devicename(device)
-
-
-NUM_FEATURES = 7
-
-
-def test_create_feature_dict() -> None:
- qc = get_benchmark("dj", 1, 5)
- features = rl.helper.create_feature_dict(qc)
- assert features
- assert len(features) == NUM_FEATURES
-
-
-def test_get_path_trained_model() -> None:
- path = rl.helper.get_path_trained_model()
- assert path.exists()
- assert isinstance(path, Path)
-
-
-def test_get_path_training_circuits() -> None:
- path = rl.helper.get_path_training_circuits()
- assert path.exists()
- assert isinstance(path, Path)
diff --git a/tests/rl/test_predictor_rl.py b/tests/rl/test_predictor_rl.py
deleted file mode 100644
index e990be6b5..000000000
--- a/tests/rl/test_predictor_rl.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import pytest
-from mqt.bench import get_benchmark
-from mqt.predictor import rl
-from qiskit import QuantumCircuit
-
-
-@pytest.mark.parametrize(
- "opt_objective",
- ["fidelity", "critical_depth", "gate_ratio", "mix"],
-)
-def test_qcompile_with_pretrained_models(opt_objective: rl.helper.reward_functions) -> None:
- qc = get_benchmark("ghz", 1, 5)
- qc_compiled, compilation_information = rl.qcompile(qc, opt_objective=opt_objective)
- assert isinstance(qc_compiled, QuantumCircuit)
- assert compilation_information is not None
-
-
-def test_instantiate_models() -> None:
- predictor = rl.Predictor()
- predictor.train_all_models(
- timesteps=100,
- reward_functions=["fidelity", "critical_depth", "mix", "gate_ratio"],
- test=True,
- )
-
-
-@pytest.mark.parametrize(
- "opt_objective",
- ["fidelity", "critical_depth", "gate_ratio", "mix"],
-)
-def test_qcompile_with_newly_trained_models(opt_objective: rl.helper.reward_functions) -> None:
- qc = get_benchmark("ghz", 1, 5)
- qc_compiled, compilation_information = rl.qcompile(qc, opt_objective=opt_objective)
- assert isinstance(qc_compiled, QuantumCircuit)
- assert compilation_information is not None
-
-
-NUM_EVALUATION_FEATURES = 32
-
-
-def test_evaluate_sample_circuit() -> None:
- qc = get_benchmark("ghz", 1, 5)
- qc.qasm(filename="test_5.qasm")
- predictor = rl.Predictor()
- res = predictor.evaluate_sample_circuit("test_5.qasm")
- assert len(res) == NUM_EVALUATION_FEATURES
diff --git a/tests/test_calibration.py b/tests/test_calibration.py
new file mode 100644
index 000000000..f6da1b1a0
--- /dev/null
+++ b/tests/test_calibration.py
@@ -0,0 +1,12 @@
+from mqt.predictor.Calibration import Calibration
+
+
+def test_calibration() -> None:
+ c = Calibration()
+ assert c.ibm_montreal_calibration
+ assert c.ibm_washington_calibration
+ assert c.oqc_lucy_calibration
+ assert c.rigetti_m2_calibration
+ assert c.ionq_harmony_calibration
+ assert c.ionq_aria1_calibration
+ assert c.quantinuum_h2_calibration
diff --git a/tests/test_evaluation.py b/tests/test_evaluation.py
new file mode 100644
index 000000000..90a245b2b
--- /dev/null
+++ b/tests/test_evaluation.py
@@ -0,0 +1,24 @@
+from pathlib import Path
+
+from mqt.bench import get_benchmark
+from mqt.predictor import rl
+from mqt.predictor.evaluation import evaluate_sample_circuit
+
+
+def test_evaluate_sample_circuit() -> None:
+ qc = get_benchmark("ghz", 1, 3)
+ filename = "test_3.qasm"
+ qc.qasm(filename=filename)
+ res = evaluate_sample_circuit(filename)
+ expected_keys = []
+ for compilation_setup in ["qiskit", "tket", "mqt-predictor_expected_fidelity", "mqt-predictor_critical_depth"]:
+ for key in ["time", "expected_fidelity", "critical_depth"]:
+ if "mqt-predictor" in compilation_setup:
+ expected_keys.append(compilation_setup + "_" + key)
+ else:
+ for device in rl.helper.get_devices():
+ expected_keys.append(compilation_setup + "_" + device["name"] + "_" + key)
+
+ assert all(key in res for key in expected_keys)
+ if Path(filename).exists():
+ Path(filename).unlink()