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#ConstraintProgramming-General #DecisionOptimization
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import numpy as np from scipy.optimize import minimize from qiskit import QuantumCircuit, transpile, Aer, execute from qiskit.visualization import plot histogram # Define the number of qubits num qubits = 4 # Define the cost operator (objective function) - Modify as needed pauli strings = ["Z" + "I"*(num qubits-1)] cost operator = pauli strings # Create a parameterized circuit for the ansatz param names = ['theta '+str(i) for i in range(2*num qubits)] param circuit = QuantumCircuit(num qubits) # Create entanglement (HXXH) for qubit in range(num qubits): param circuit.h(qubit) param circuit.x(qubit) for qubit in range(num qubits - 1): param circuit.cx(qubit, qubit + 1) for qubit in range(num qubits): param circuit.x(qubit) param circuit.h(qubit) # Define the QAOA function def qaoa(params): circuit = param circuit.copy() for p in range(0, len(params), 2): beta, gamma = params[p], params[p+1] for qubit in range(num qubits): circuit.ry(2 * beta, qubit) circuit.rz(2 * gamma, qubit) backend = Aer.get backend('statevector simulator') job = execute(circuit, backend) result = job.result() statevector = result.get statevector(circuit) objective value = statevector[0].real # Modify this based on your problem return objective value # Define the optimization problem def objective(params): return -qaoa(params) # Optimize the parameters init params = np.random.rand(2*num qubits) # Initial parameters result = minimize(objective, init params, method='COBYLA') # Extract optimal parameters and objective value optimal params = result.x optimal objective = -result.fun print("Optimal parameters:", optimal params) print("Optimal objective value:", optimal objective)