48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
from qiskit import QuantumCircuit, Aer, transpile, assemble
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from qiskit.aqua.components.optimizers import COBYLA
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from qiskit.circuit import Parameter
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import numpy as np
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# Define the desired probability distribution
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target_probs = [0.1666666666] * 6
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# Define the quantum circuit
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circ = QuantumCircuit(6, 6)
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circ.h(range(6))
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# Add the parameterized Ansatz using Parameter
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params = [Parameter(f'var{i}') for i in range(6)]
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circ.rx(params[0], 0)
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circ.rx(params[1], 1)
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circ.rx(params[2], 2)
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circ.rx(params[3], 3)
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circ.rx(params[4], 4)
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circ.rx(params[5], 5)
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# Define the optimization objective function
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def objective_function(params):
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# Update the circuit with new parameters
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updated_circ = circ.bind_parameters({params[i]: optimal_params[i] for i in range(6)})
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# Compile and run the circuit
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t_circ = transpile(updated_circ, Aer.get_backend('qasm_simulator'))
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qobj = assemble(t_circ)
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result = Aer.get_backend('qasm_simulator').run(qobj).result()
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# Calculate the probability distribution
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counts = result.get_counts()
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probs = [counts.get(format(i, '06b'), 0) / result.shots for i in range(2 ** 6)]
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# Calculate the objective function (sum of squared differences)
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return sum((probs[i] - target_probs[i]) ** 2 for i in range(2 ** 6))
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# Optimize the circuit parameters
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optimizer = COBYLA(maxiter=1000)
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optimal_params = optimizer.optimize(num_vars=6, objective_function=objective_function)
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# Print the optimal parameters
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print("Optimal Parameters:", optimal_params)
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