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,
),
)
