Create quantum_neural_network.py

quantum_integration/quantum_machine_learning/quantum_neural_network.py

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+from qiskit import QuantumCircuit, Aer, execute
+from qiskit.circuit.library import TwoLocal
+import numpy as np
+from sklearn.datasets import make_moons
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+def quantum_neural_network(X_train, y_train, X_test):
+    # Create a quantum circuit for the neural network
+    num_qubits = 2  # Number of qubits
+    qc = QuantumCircuit(num_qubits)
+
+    # Define a parameterized circuit (ansatz)
+    ansatz = TwoLocal(num_qubits, rotation='ry', entanglement='cz', reps=2)
+
+    # Train the model (this is a placeholder for actual training logic)
+    # In practice, you would use a quantum optimizer to adjust the parameters
+    qc.compose(ansatz, inplace=True)
+
+    # Measure the output
+    qc.measure_all()
+
+    # Execute the circuit
+    backend = Aer.get_backend('qasm_simulator')
+    result = execute(qc, backend, shots=1024).result()
+    counts = result.get_counts()
+
+    # Process results to make predictions (this is a placeholder)
+    predictions = [1 if count[0] == '1' else 0 for count in counts.keys()]
+
+    return predictions
+
+if __name__ == "__main__":
+    # Generate synthetic data for classification
+    X, y = make_moons(n_samples=100, noise=0.1, random_state=42)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Train the quantum neural network
+    predictions = quantum_neural_network(X_train, y_train, X_test)
+
+    # Evaluate the model
+    accuracy = accuracy_score(y_test, predictions)
+    print("Test Accuracy:", accuracy)