Create Quantum_optimization.py

Quantum_optimization.py

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+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}")