Quantum_optimization.py

import numpy as np
from qiskit import Aer
from qiskit import QuantumCircuit
from qiskit.algorithms import QAOA
from qiskit_optimization.algorithms import MinimumEigenOptimizer
from qiskit_optimization import QuadraticProgram
from qiskit.aqua.operators import Z, X
from qiskit.aqua.algorithms import Grover
from qiskit import execute

# Quantum Optimization: MaxCut Problem
def create_maxcut_problem(num_nodes, edges, weights):
    """
    Creates a QuadraticProgram for the MaxCut optimization problem.
    :param num_nodes: number of nodes in the graph
    :param edges: list of tuples representing edges
    :param weights: dictionary of edge weights
    :return: QuadraticProgram instance
    """
    qp = QuadraticProgram()
    
    # Define binary variables for each node
    for i in range(num_nodes):
        qp.binary_var(f'x{i}')
    
    # Set the quadratic objective function based on edges and weights
    for i, j in edges:
        weight = weights.get((i, j), 1)  # Default weight is 1 if not specified
        qp.minimize(constant=0, linear=[], quadratic={(f'x{i}', f'x{j}'): weight})
    
    return qp

def quantum_optimization(qp):
    """
    Performs quantum optimization using QAOA (Quantum Approximate Optimization Algorithm).
    :param qp: QuadraticProgram to optimize
    :return: Optimal solution and its value
    """
    # Set up the quantum instance and QAOA
    backend = Aer.get_backend('statevector_simulator')
    qaoa = QAOA(quantum_instance=backend, reps=3)  # Increase reps for better optimization
    
    # Use the MinimumEigenOptimizer to solve the problem with QAOA
    optimizer = MinimumEigenOptimizer(qaoa)
    result = optimizer.solve(qp)
    
    return result

def quantum_machine_learning(X_train, y_train, X_test, y_test):
    """
    Simulate a quantum-enhanced machine learning model by performing quantum optimization
    alongside classical machine learning models.
    :param X_train: training data features
    :param y_train: training data labels
    :param X_test: test data features
    :param y_test: test data labels
    :return: SVM model score and quantum optimization result
    """
    # Classical SVM as a baseline for performance comparison
    from sklearn.svm import SVC
    clf = SVC(kernel='linear')
    clf.fit(X_train, y_train)
    score = clf.score(X_test, y_test)
    
    # Perform Quantum Optimization (MaxCut)
    maxcut_problem = create_maxcut_problem(4, [(0, 1), (1, 2), (2, 3), (3, 0)], {(0, 1): 1, (1, 2): 1, (2, 3): 1, (3, 0): 1})
    quantum_result = quantum_optimization(maxcut_problem)
    
    return score, quantum_result

# Example to create a problem and solve it
if __name__ == '__main__':
    # Sample data for testing the quantum optimization integration
    X_train = np.random.rand(100, 5)
    y_train = np.random.choice([0, 1], size=100)
    X_test = np.random.rand(50, 5)
    y_test = np.random.choice([0, 1], size=50)
    
    # Simulate Quantum-enhanced Machine Learning (using SVM and Quantum Optimization)
    accuracy, quantum_result = quantum_machine_learning(X_train, y_train, X_test, y_test)
    
    print(f"Accuracy of SVM model: {accuracy:.2f}")
    print(f"Quantum Optimization Result: {quantum_result}")