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)