def run_circuit_and_tomography(circuit, qubits, method):
choi_ideal = Choi(circuit).data
qst = tomo.process_tomography_circuits(circuit, qubits)
job = qiskit.execute(qst, Aer.get_backend('qasm_simulator'), shots=5000)
tomo_fit = tomo.ProcessTomographyFitter(job.result(), qst)
choi = tomo_fit.fit(method=method).data
return (choi, choi_ideal)
def extract_choi_matrix(result: Result,
qpt_qcs_list: List[List[QuantumCircuit]],
meas_labels: List[str]) -> Choi:
""" Estimate quantum channel from experiment.
Args:
result: Result of tomography experiment.
qpt_qcs_list: Process tomography circuits.
meas_labels: Measurement labels.
Note:
Need to:
pip install cvxopt
Yields:
Quantum channel in Choi matrix representation.
"""
def format_result(data_index, chunk):
"""Create new result object from partial result and marginalize."""
new_result = deepcopy(result)
new_result.results = []
new_result.results.extend(result.results[data_index:data_index +
chunk])
return marginal_counts(new_result, __reserved_registers)
# readout error mitigation
if len(meas_labels) > 0:
mit_result = format_result(data_index=0, chunk=len(meas_labels))
meas_fitter = mit.CompleteMeasFitter(mit_result,
meas_labels,
qubit_list=[0, 1],
circlabel='mcal')
print('readout fidelity = %.3f' % meas_fitter.readout_fidelity())
else:
meas_fitter = None
# format qpt result
qpt_results = []
for ind, qpt_qcs in enumerate(qpt_qcs_list):
qpt_result = format_result(data_index=len(meas_labels) +
ind * len(qpt_qcs),
chunk=len(qpt_qcs))
if meas_fitter:
qpt_results.append(meas_fitter.filter.apply(qpt_result))
else:
qpt_results.append(qpt_result)
# process tomography
for qpt_result, qpt_circuit in zip(qpt_results, qpt_qcs_list):
process_fitter = tomo.ProcessTomographyFitter(qpt_result,
circuits=qpt_circuit)
qpt_choi = process_fitter.fit(method='cvx', solver='CVXOPT')
yield qpt_choi
def run_circuit_and_tomography(circuit, qubits):
job = qiskit.execute(circuit, Aer.get_backend('unitary_simulator'))
U = job.result().get_unitary(circuit)
choi_ideal = outer(U.ravel(order='F'))
qst = tomo.process_tomography_circuits(circuit, qubits)
job = qiskit.execute(qst, Aer.get_backend('qasm_simulator'), shots=5000)
tomo_fit = tomo.ProcessTomographyFitter(job.result(), qst)
choi_cvx = tomo_fit.fit(method='cvx').data
choi_mle = tomo_fit.fit(method='lstsq').data
return (choi_cvx, choi_mle, choi_ideal)