def test_floquet_parse_result_bad_metric(): q_00, q_01, q_02, q_03 = [cirq.GridQubit(0, index) for index in range(4)] gate = cirq.FSimGate(theta=np.pi / 4, phi=0.0) request = FloquetPhasedFSimCalibrationRequest( gate=gate, pairs=((q_00, q_01), (q_02, q_03)), options=FloquetPhasedFSimCalibrationOptions( characterize_theta=True, characterize_zeta=True, characterize_chi=False, characterize_gamma=False, characterize_phi=True, ), ) result = cirq_google.CalibrationResult( code=cirq_google.api.v2.calibration_pb2.SUCCESS, error_message=None, token=None, valid_until=None, metrics=cirq_google.Calibration( cirq_google.api.v2.metrics_pb2.MetricsSnapshot(metrics=[ cirq_google.api.v2.metrics_pb2.Metric( name='angles', targets=[ '1000gerbils', ], values=[ cirq_google.api.v2.metrics_pb2.Value(str_val='100_10'), ], ) ])), ) with pytest.raises(ValueError, match='Unknown metric name 1000gerbils'): _ = request.parse_result(result)
def test_xeb_parse_result_failure(): gate = cirq.FSimGate(theta=np.pi / 4, phi=0.0) request = XEBPhasedFSimCalibrationRequest( gate=gate, pairs=(), options=XEBPhasedFSimCalibrationOptions( fsim_options=XEBPhasedFSimCharacterizationOptions( characterize_theta=False, characterize_zeta=False, characterize_chi=False, characterize_gamma=False, characterize_phi=True, )), ) result = cirq_google.CalibrationResult( code=cirq_google.api.v2.calibration_pb2.ERROR_CALIBRATION_FAILED, error_message="Test message", token=None, valid_until=None, metrics=cirq_google.Calibration(), ) with pytest.raises(PhasedFSimCalibrationError, match='Test message'): request.parse_result(result)
def _load_xeb_results_textproto() -> cirq_google.CalibrationResult: with open(os.path.dirname(__file__) + '/test_data/xeb_results.textproto') as f: metrics_snapshot = text_format.Parse( f.read(), cirq_google.api.v2.metrics_pb2.MetricsSnapshot()) return cirq_google.CalibrationResult( code=cirq_google.api.v2.calibration_pb2.SUCCESS, error_message=None, token=None, valid_until=None, metrics=cirq_google.Calibration(metrics_snapshot), )
def test_run_floquet_characterization_for_moments(): q_00, q_01, q_02, q_03 = [cirq.GridQubit(0, index) for index in range(4)] gate = cirq.FSimGate(theta=np.pi / 4, phi=0.0) circuit = cirq.Circuit([gate.on(q_00, q_01), gate.on(q_02, q_03)]) options = FloquetPhasedFSimCalibrationOptions( characterize_theta=True, characterize_zeta=True, characterize_chi=False, characterize_gamma=False, characterize_phi=True, ) job = cirq_google.engine.EngineJob('', '', '', None) job._calibration_results = [ cirq_google.CalibrationResult( code=cirq_google.api.v2.calibration_pb2.SUCCESS, error_message=None, token=None, valid_until=None, metrics=cirq_google.Calibration( cirq_google.api.v2.metrics_pb2.MetricsSnapshot(metrics=[ cirq_google.api.v2.metrics_pb2.Metric( name='angles', targets=[ '0_qubit_a', '0_qubit_b', '0_theta_est', '0_zeta_est', '0_phi_est', '1_qubit_a', '1_qubit_b', '1_theta_est', '1_zeta_est', '1_phi_est', ], values=[ cirq_google.api.v2.metrics_pb2.Value( str_val='0_0'), cirq_google.api.v2.metrics_pb2.Value( str_val='0_1'), cirq_google.api.v2.metrics_pb2.Value( double_val=0.1), cirq_google.api.v2.metrics_pb2.Value( double_val=0.2), cirq_google.api.v2.metrics_pb2.Value( double_val=0.3), cirq_google.api.v2.metrics_pb2.Value( str_val='0_2'), cirq_google.api.v2.metrics_pb2.Value( str_val='0_3'), cirq_google.api.v2.metrics_pb2.Value( double_val=0.4), cirq_google.api.v2.metrics_pb2.Value( double_val=0.5), cirq_google.api.v2.metrics_pb2.Value( double_val=0.6), ], ) ])), ) ] engine = mock.MagicMock(spec=cirq_google.Engine) engine.run_calibration.return_value = job circuit_with_calibration, requests = workflow.run_floquet_characterization_for_moments( circuit, engine, 'qproc', cirq_google.FSIM_GATESET, options=options) assert requests == [ PhasedFSimCalibrationResult( parameters={ (q_00, q_01): PhasedFSimCharacterization(theta=0.1, zeta=0.2, chi=None, gamma=None, phi=0.3), (q_02, q_03): PhasedFSimCharacterization(theta=0.4, zeta=0.5, chi=None, gamma=None, phi=0.6), }, gate=gate, options=options, ) ] assert circuit_with_calibration.circuit == circuit assert circuit_with_calibration.moment_to_calibration == [0]
def test_run_characterization(): q_00, q_01, q_02, q_03 = [cirq.GridQubit(0, index) for index in range(4)] gate = cirq.FSimGate(theta=np.pi / 4, phi=0.0) request = FloquetPhasedFSimCalibrationRequest( gate=gate, pairs=((q_00, q_01), (q_02, q_03)), options=FloquetPhasedFSimCalibrationOptions( characterize_theta=True, characterize_zeta=True, characterize_chi=False, characterize_gamma=False, characterize_phi=True, ), ) result = cirq_google.CalibrationResult( code=cirq_google.api.v2.calibration_pb2.SUCCESS, error_message=None, token=None, valid_until=None, metrics=cirq_google.Calibration( cirq_google.api.v2.metrics_pb2.MetricsSnapshot(metrics=[ cirq_google.api.v2.metrics_pb2.Metric( name='angles', targets=[ '0_qubit_a', '0_qubit_b', '0_theta_est', '0_zeta_est', '0_phi_est', '1_qubit_a', '1_qubit_b', '1_theta_est', '1_zeta_est', '1_phi_est', ], values=[ cirq_google.api.v2.metrics_pb2.Value(str_val='0_0'), cirq_google.api.v2.metrics_pb2.Value(str_val='0_1'), cirq_google.api.v2.metrics_pb2.Value(double_val=0.1), cirq_google.api.v2.metrics_pb2.Value(double_val=0.2), cirq_google.api.v2.metrics_pb2.Value(double_val=0.3), cirq_google.api.v2.metrics_pb2.Value(str_val='0_2'), cirq_google.api.v2.metrics_pb2.Value(str_val='0_3'), cirq_google.api.v2.metrics_pb2.Value(double_val=0.4), cirq_google.api.v2.metrics_pb2.Value(double_val=0.5), cirq_google.api.v2.metrics_pb2.Value(double_val=0.6), ], ) ])), ) job = cirq_google.engine.EngineJob('', '', '', None) job._calibration_results = [result] engine = mock.MagicMock(spec=cirq_google.Engine) engine.run_calibration.return_value = job progress_calls = [] def progress(step: int, steps: int) -> None: progress_calls.append((step, steps)) actual = workflow.run_calibrations([request], engine, 'qproc', cirq_google.FSIM_GATESET, progress_func=progress) expected = [ PhasedFSimCalibrationResult( parameters={ (q_00, q_01): PhasedFSimCharacterization(theta=0.1, zeta=0.2, chi=None, gamma=None, phi=0.3), (q_02, q_03): PhasedFSimCharacterization(theta=0.4, zeta=0.5, chi=None, gamma=None, phi=0.6), }, gate=gate, options=FloquetPhasedFSimCalibrationOptions( characterize_theta=True, characterize_zeta=True, characterize_chi=False, characterize_gamma=False, characterize_phi=True, ), ) ] assert actual == expected assert progress_calls == [(1, 1)]
def test_floquet_parse_result(): q_00, q_01, q_02, q_03 = [cirq.GridQubit(0, index) for index in range(4)] gate = cirq.FSimGate(theta=np.pi / 4, phi=0.0) request = FloquetPhasedFSimCalibrationRequest( gate=gate, pairs=((q_00, q_01), (q_02, q_03)), options=FloquetPhasedFSimCalibrationOptions( characterize_theta=True, characterize_zeta=True, characterize_chi=False, characterize_gamma=False, characterize_phi=True, ), ) result = cirq_google.CalibrationResult( code=cirq_google.api.v2.calibration_pb2.SUCCESS, error_message=None, token=None, valid_until=None, metrics=cirq_google.Calibration( cirq_google.api.v2.metrics_pb2.MetricsSnapshot(metrics=[ cirq_google.api.v2.metrics_pb2.Metric( name='angles', targets=[ '0_qubit_a', '0_qubit_b', '0_theta_est', '0_zeta_est', '0_phi_est', '1_qubit_a', '1_qubit_b', '1_theta_est', '1_zeta_est', '1_phi_est', ], values=[ cirq_google.api.v2.metrics_pb2.Value(str_val='0_0'), cirq_google.api.v2.metrics_pb2.Value(str_val='0_1'), cirq_google.api.v2.metrics_pb2.Value(double_val=0.1), cirq_google.api.v2.metrics_pb2.Value(double_val=0.2), cirq_google.api.v2.metrics_pb2.Value(double_val=0.3), cirq_google.api.v2.metrics_pb2.Value(str_val='0_2'), cirq_google.api.v2.metrics_pb2.Value(str_val='0_3'), cirq_google.api.v2.metrics_pb2.Value(double_val=0.4), cirq_google.api.v2.metrics_pb2.Value(double_val=0.5), cirq_google.api.v2.metrics_pb2.Value(double_val=0.6), ], ) ])), ) assert request.parse_result(result) == PhasedFSimCalibrationResult( parameters={ (q_00, q_01): PhasedFSimCharacterization(theta=0.1, zeta=0.2, chi=None, gamma=None, phi=0.3), (q_02, q_03): PhasedFSimCharacterization(theta=0.4, zeta=0.5, chi=None, gamma=None, phi=0.6), }, gate=gate, options=FloquetPhasedFSimCalibrationOptions( characterize_theta=True, characterize_zeta=True, characterize_chi=False, characterize_gamma=False, characterize_phi=True, ), )