From 4c2ed76ebd6001a8e8a5cdabf08928bee0f9040b Mon Sep 17 00:00:00 2001
From: KOSASIH <kosasihg88@gmail.com>
Date: Sun, 19 Jan 2025 17:56:53 +0700
Subject: [PATCH] Create variational_circuit.py

---
 quantum/circuits/variational_circuit.py | 78 +++++++++++++++++++++++++
 1 file changed, 78 insertions(+)
 create mode 100644 quantum/circuits/variational_circuit.py

diff --git a/quantum/circuits/variational_circuit.py b/quantum/circuits/variational_circuit.py
new file mode 100644
index 000000000..8b047ed94
--- /dev/null
+++ b/quantum/circuits/variational_circuit.py
@@ -0,0 +1,78 @@
+# variational_circuit.py
+import numpy as np
+from qiskit import QuantumCircuit, Aer, execute
+from qiskit.visualization import plot_histogram
+from qiskit.quantum_info import Statevector
+from qiskit.algorithms import VQE
+from qiskit.algorithms.optimizers import SLSQP
+from qiskit.primitives import Sampler
+
+def create_variational_circuit(num_qubits, params):
+    """
+    Create a parameterized variational circuit.
+    
+    Parameters:
+    - num_qubits: Number of qubits in the circuit
+    - params: List of parameters for the variational gates
+    
+    Returns:
+    - QuantumCircuit: The constructed variational circuit
+    """
+    circuit = QuantumCircuit(num_qubits)
+
+    # Apply parameterized RY gates
+    for i in range(num_qubits):
+        circuit.ry(params[i], i)
+
+    # Add entangling gates (CNOTs)
+    for i in range(num_qubits - 1):
+        circuit.cx(i, i + 1)
+
+    return circuit
+
+def run_variational_algorithm(num_qubits, initial_params):
+    """
+    Run a variational algorithm (e.g., VQE) using the variational circuit.
+    
+    Parameters:
+    - num_qubits: Number of qubits in the circuit
+    - initial_params: Initial parameters for the variational circuit
+    
+    Returns:
+    - optimal_value: The minimum eigenvalue found
+    - optimal_params: The optimal parameters for the variational circuit
+    """
+    # Create a variational circuit
+    circuit = create_variational_circuit(num_qubits, initial_params)
+
+    # Define the optimizer
+    optimizer = SLSQP(maxiter=100)
+
+    # Create a VQE instance
+    vqe = VQE(circuit, optimizer=optimizer, quantum_instance=Aer.get_backend('aer_simulator'))
+
+    # Run VQE
+    result = vqe.compute_minimum_eigenvalue()
+
+    return result.eigenvalue, result.optimal_point
+
+def visualize_results(optimal_value, optimal_params):
+    """
+    Visualize the results of the variational algorithm.
+    
+    Parameters:
+    - optimal_value: The minimum eigenvalue found
+    - optimal_params: The optimal parameters for the variational circuit
+    """
+    print("Minimum Eigenvalue:", optimal_value)
+    print("Optimal Parameters:", optimal_params)
+
+if __name__ == "__main__":
+    num_qubits = 2  # Number of qubits for the variational circuit
+    initial_params = np.random.rand(num_qubits) * np.pi  # Random initial parameters
+
+    # Run the variational algorithm
+    optimal_value, optimal_params = run_variational_algorithm(num_qubits, initial_params)
+
+    # Visualize the results
+    visualize_results(optimal_value, optimal_params)