def create_experiment_data(self, iq_data: List[Any], single_shot: bool = False):
"""Populate avg_iq_data to use it for testing.
Args:
iq_data: A List of IQ data.
single_shot: Indicates if the data is single-shot or not.
"""
results = []
if not single_shot:
for circ_data in iq_data:
res = ExperimentResult(
success=True,
meas_level=1,
meas_return="avg",
data=ExperimentResultData(memory=circ_data),
header=self.header,
shots=1024,
)
results.append(res)
else:
for circ_data in iq_data:
res = ExperimentResult(
success=True,
meas_level=1,
meas_return="single",
data=ExperimentResultData(memory=circ_data),
header=self.header,
shots=1024,
)
results.append(res)
# pylint: disable=attribute-defined-outside-init
self.iq_experiment = ExperimentData(FakeExperiment())
self.iq_experiment.add_data(Result(results=results, **self.base_result_args))
def setUp(self):
"""
Setup internal variables and a fake simulation. Aer is used to get the
structure of the qiskit.Result. The IQ data is generated using gaussian
random number generators.
"""
self.shots = 52
self.qubits = [0, 1]
meas_cal, _ = circuits.tensored_meas_cal([[0], [1]])
backend = Aer.get_backend('qasm_simulator')
job = qiskit.execute(meas_cal,
backend=backend,
shots=self.shots,
meas_level=1)
self.cal_results = job.result()
i0, q0, i1, q1 = 0., -1., 0., 1.
ground = utils.create_shots(i0, q0, 0.1, 0.1, self.shots, self.qubits)
excited = utils.create_shots(i1, q1, 0.1, 0.1, self.shots, self.qubits)
self.cal_results.results[0].meas_level = 1
self.cal_results.results[1].meas_level = 1
self.cal_results.results[0].data = ExperimentResultData(memory=ground)
self.cal_results.results[1].data = ExperimentResultData(memory=excited)
def setUp(self):
experiment_result_data = ExperimentResultData.from_dict(
{'counts': {
'0x0': 42
}})
experiment_result_data_2 = ExperimentResultData.from_dict(
{'counts': {
'0x1': 42
}})
header_1 = Obj.from_dict({'name': 'Test1'})
header_2 = Obj.from_dict({'name': 'Test2'})
self.experiment_result_dictionary_1 = {
'name': 'Test1',
'shots': 42,
'data': experiment_result_data,
'status': 'DONE',
'success': True,
'time_taken': 0.42,
'header': header_1
}
self.experiment_result_dictionary_2 = {
'name': 'Test2',
'shots': 23,
'data': experiment_result_data_2,
'status': 'DONE',
'success': True,
'time_taken': 0.12,
'header': header_2
}
self.experiment_result_1 = ExperimentResult(
**self.experiment_result_dictionary_1)
self.experiment_result_2 = ExperimentResult(
**self.experiment_result_dictionary_2)
def setUp(self):
"""Here, single-shots average to points at plus/minus 1.
The setting corresponds to four single-shots done on two qubits.
"""
super().setUp()
circ_es = ExperimentResultData(memory=[[1.0, 1.0], [-1.0, 1.0]])
self._sig_gs = np.array([1.0, -1.0]) / np.sqrt(2.0)
circ_gs = ExperimentResultData(memory=[[-1.0, -1.0], [1.0, -1.0]])
self._sig_es = np.array([-1.0, 1.0]) / np.sqrt(2.0)
circ_x90p = ExperimentResultData(memory=[[0.0, 0.0], [0.0, 0.0]])
self._sig_x90 = np.array([0, 0])
circ_x45p = ExperimentResultData(memory=[[-0.5, -0.5], [0.5, -0.5]])
self._sig_x45 = np.array([0.5, -0.5]) / np.sqrt(2.0)
res_es = ExperimentResult(
shots=4,
success=True,
meas_level=1,
meas_return="avg",
data=circ_es,
header=self.header,
)
res_gs = ExperimentResult(
shots=4,
success=True,
meas_level=1,
meas_return="avg",
data=circ_gs,
header=self.header,
)
res_x90p = ExperimentResult(
shots=4,
success=True,
meas_level=1,
meas_return="avg",
data=circ_x90p,
header=self.header,
)
res_x45p = ExperimentResult(
shots=4,
success=True,
meas_level=1,
meas_return="avg",
data=circ_x45p,
header=self.header,
)
self.data = ExperimentData(FakeExperiment())
self.data.add_data(
Result(results=[res_es, res_gs, res_x90p, res_x45p],
**self.base_result_args))
def get_experiment_results(self, qi_job: QIJob) -> List[ExperimentResult]:
""" Get results from experiments from the Quantum-inspire platform.
Args:
qi_job: A job that has already been submitted and which execution is completed.
Raises:
QisKitBackendError: If an error occurred during execution by the backend.
Returns:
A list of experiment results; containing the data, execution time, status, etc.
"""
jobs = self.__api.get_jobs_from_project(int(qi_job.job_id()))
results = [self.__api.get_result_from_job(job['id']) for job in jobs]
experiment_results = []
for result, job in zip(results, jobs):
if not result.get('histogram', {}):
raise QisKitBackendError(
'Result from backend contains no histogram data!\n{}'.format(result.get('raw_text')))
user_data = json.loads(str(job.get('user_data')))
measurements = user_data.pop('measurements')
histogram_obj, memory_data = self.__convert_result_data(result, measurements)
full_state_histogram_obj = self.__convert_histogram(result, measurements)
experiment_result_data = ExperimentResultData(counts=histogram_obj,
probabilities=full_state_histogram_obj,
memory=memory_data)
header = Obj.from_dict(user_data)
experiment_result_dictionary = {'name': job.get('name'), 'seed': 42, 'shots': job.get('number_of_shots'),
'data': experiment_result_data, 'status': 'DONE', 'success': True,
'time_taken': result.get('execution_time_in_seconds'), 'header': header}
experiment_results.append(ExperimentResult(**experiment_result_dictionary))
return experiment_results
def _get_experiment_results(self, jobs: List[Dict[str, Any]]) -> List[ExperimentResult]:
""" Get results from experiments from the Quantum Inspire platform for one or more jobs.
:param jobs: A list of jobs
:raises QiskitBackendError: If an error occurred while executing the job on the Quantum Inspire backend.
:return:
A list of experiment results; containing the data, execution time, status, etc. for the list of jobs.
"""
results = [self.__api.get_result_from_job(job['id']) for job in jobs]
experiment_results = []
for result, job in zip(results, jobs):
if not result.get('histogram', {}):
raise QiskitBackendError(
'Result from backend contains no histogram data!\n{}'.format(result.get('raw_text')))
user_data = json.loads(str(job.get('user_data')))
measurements = user_data.pop('measurements')
histogram_obj, memory_data = self.__convert_result_data(result, measurements)
full_state_histogram_obj = self.__convert_histogram(result, measurements)
calibration = self.__api.get_calibration_from_result(result['id'])
experiment_result_data = ExperimentResultData(counts=histogram_obj,
memory=memory_data,
probabilities=full_state_histogram_obj,
calibration=calibration)
header = QobjExperimentHeader.from_dict(user_data)
experiment_result_dictionary = {'name': job.get('name'), 'seed': 42, 'shots': job.get('number_of_shots'),
'data': experiment_result_data, 'status': 'DONE', 'success': True,
'time_taken': result.get('execution_time_in_seconds'), 'header': header}
experiment_results.append(ExperimentResult(**experiment_result_dictionary))
return experiment_results
def backendresult_to_qiskit_resultdata(
res: BackendResult,
header: "QobjExperimentHeader",
final_map: Optional[Dict[UnitID, UnitID]],
) -> ExperimentResultData:
data: Dict[str, Any] = dict()
if res.contains_state_results:
qbits = (
_gen_uids(header.qreg_sizes, Qubit) if hasattr(header, "qreg_sizes") else []
)
if final_map:
qbits = [final_map[q] for q in qbits]
stored_res = res.get_result(qbits)
if stored_res.state is not None:
data["statevector"] = stored_res.state
if stored_res.unitary is not None:
data["unitary"] = stored_res.unitary
if res.contains_measured_results:
cbits = (
_gen_uids(header.creg_sizes, Bit) if hasattr(header, "creg_sizes") else []
)
if final_map:
cbits = [final_map[c] for c in cbits]
stored_res = res.get_result(cbits)
if stored_res.shots is not None:
data["memory"] = [hex(i) for i in stored_res.shots.to_intlist()]
data["counts"] = dict(Counter(data["memory"]))
elif stored_res.counts is not None:
data["counts"] = {
hex(i.to_intlist()[0]): f for i, f in stored_res.counts.items()
}
return ExperimentResultData(**data)
def setUp(self):
"""Setup variables used for testing."""
super().setUp()
mem1 = ExperimentResultData(memory=[
[[1103260.0, -11378508.0], [2959012.0, -16488753.0]],
[[442170.0, -19283206.0], [-5279410.0, -15339630.0]],
[[3016514.0, -14548009.0], [-3404756.0, -16743348.0]],
])
mem2 = ExperimentResultData(memory=[
[[5131962.0, -16630257.0], [4438870.0, -13752518.0]],
[[3415985.0, -16031913.0], [2942458.0, -15840465.0]],
[[5199964.0, -14955998.0], [4030843.0, -14538923.0]],
])
res1 = ExperimentResult(shots=3,
success=True,
meas_level=1,
data=mem1,
header=self.header)
res2 = ExperimentResult(shots=3,
success=True,
meas_level=1,
data=mem2,
header=self.header)
self.result_lvl1 = Result(results=[res1, res2],
**self.base_result_args)
raw_counts1 = {"0x0": 4, "0x2": 6}
raw_counts2 = {"0x0": 2, "0x2": 8}
data1 = ExperimentResultData(counts=dict(**raw_counts1))
data2 = ExperimentResultData(counts=dict(**raw_counts2))
res1 = ExperimentResult(shots=10,
success=True,
meas_level=2,
data=data1,
header=self.header)
res2 = ExperimentResult(shots=10,
success=True,
meas_level=2,
data=data2,
header=self.header)
self.exp_data_lvl2 = ExperimentData(FakeExperiment())
self.exp_data_lvl2.add_data(
Result(results=[res1, res2], **self.base_result_args))
def setUp(self):
"""Setup some IQ data."""
super().setUp()
mem_avg = ExperimentResultData(
memory=[[-539698.0, -153030784.0], [5541283.0, -160369600.0]])
mem_single = ExperimentResultData(memory=[
[[-56470872.0, -136691568.0], [-53407256.0, -176278624.0]],
[[-34623272.0, -151247824.0], [-36650644.0, -170559312.0]],
[[42658720.0, -153054640.0], [29689970.0, -174671824.0]],
[[-47387248.0, -177826640.0], [-62149124.0, -165909728.0]],
[[-51465408.0, -148338000.0], [23157112.0, -165826736.0]],
[[51426688.0, -142703104.0], [34330920.0, -185572592.0]],
])
res_single = ExperimentResult(
shots=3,
success=True,
meas_level=1,
meas_return="single",
data=mem_single,
header=self.header,
)
res_avg = ExperimentResult(shots=6,
success=True,
meas_level=1,
meas_return="avg",
data=mem_avg,
header=self.header)
# result_single = Result(results=[res_single], **self.base_result_args)
# result_avg = Result(results=[res_avg], **self.base_result_args)
self.exp_data_single = ExperimentData(FakeExperiment())
self.exp_data_single.add_data(
Result(results=[res_single], **self.base_result_args))
self.exp_data_avg = ExperimentData(FakeExperiment())
self.exp_data_avg.add_data(
Result(results=[res_avg], **self.base_result_args))
def test_apply(self):
"""
Set-up a discriminator based on simulated data, train it and then
discriminate the calibration data.
"""
meas_cal, _ = circuits.tensored_meas_cal([[0], [1]])
backend = Aer.get_backend('qasm_simulator')
job = qiskit.execute(meas_cal,
backend=backend,
shots=self.shots,
meas_level=1)
cal_results = job.result()
i0, q0, i1, q1 = 0., -1., 0., 1.
ground = utils.create_shots(i0, q0, 0.1, 0.1, self.shots, self.qubits)
excited = utils.create_shots(i1, q1, 0.1, 0.1, self.shots, self.qubits)
cal_results.results[0].meas_level = 1
cal_results.results[1].meas_level = 1
cal_results.results[0].data = ExperimentResultData(memory=ground)
cal_results.results[1].data = ExperimentResultData(memory=excited)
discriminator = LinearIQDiscriminator(cal_results, self.qubits,
['00', '11'])
d_filter = DiscriminationFilter(discriminator)
self.assertEqual(cal_results.results[0].meas_level, 1)
new_results = d_filter.apply(cal_results)
self.assertEqual(new_results.results[0].meas_level, 2)
counts_00 = new_results.results[0].data.counts.to_dict()['0x0']
counts_11 = new_results.results[1].data.counts.to_dict()['0x3']
self.assertEqual(counts_00, self.shots)
self.assertEqual(counts_11, self.shots)
def qiskit_experiment_result_from_quasar_probability_histogram(
h: QuasarProbabilityHistogram, bit_map: Dict[int,
int]) -> ExperimentResult:
# it's possible for a null bit map if there are no measurement gates, in which
# case the aer simulator returns {} for counts since nothing is measured
qiskit_counts = remap_probability_histogram(
h, bit_map) if len(bit_map) > 0 else {}
experiment_data = ExperimentResultData(counts=qiskit_counts)
experiment_result = ExperimentResult(shots=h.nmeasurement,
success=True,
data=experiment_data)
# meas_level = MeasLevel.CLASSIFIED, meas_return = MeasLevel.AVERAGE ?
# TODO must set jobj_id and job_id
return experiment_result
def _generate_experiment_result(qlm_result, metadata):
"""
Generates a Qiskit experiment result.
Args:
qlm_result: qat.core.wrappers.Result object which data is aggregated
metadata: metadata of the circuit of the experiment
Returns:
An ExperimentResult structure.
"""
samples = [hex(s.state.state) for s in qlm_result.raw_data]
counts = dict(Counter(samples))
data = ExperimentResultData.from_dict({"counts": counts})
return ExperimentResult(
shots=len(qlm_result.raw_data),
success=True,
data=data,
header=QobjExperimentHeader(**metadata),
)
def apply(self, raw_data: Result) -> Result:
"""
Create a new result from the raw_data by converting level 1 data to
level 2 data.
Args:
raw_data: list of qiskit.Result or qiskit.Result.
Returns:
A list of qiskit.Result or qiskit.Result.
"""
new_results = deepcopy(raw_data)
to_be_discriminated = []
# Extract all the meas level 1 data from the Result.
shots_per_experiment_result = []
for result in new_results.results:
if result.meas_level == 1:
shots_per_experiment_result.append(result.shots)
to_be_discriminated.append(result)
new_results.results = to_be_discriminated
x_data = self.discriminator.get_xdata(new_results, 2)
y_data = self.discriminator.discriminate(x_data)
start = 0
for idx, n_shots in enumerate(shots_per_experiment_result):
memory = y_data[start:(start + n_shots)]
counts = Obj.from_dict(self.count(memory))
new_results.results[idx].data = ExperimentResultData(counts=counts,
memory=memory)
start += n_shots
for result in new_results.results:
result.meas_level = 2
return new_results
def result(self):
experiment_results: List[ExperimentResult] = []
task: AwsQuantumTask
qasm_experiment: QasmQobjExperiment
for task, qasm_experiment in zip(self._tasks, self._qobj.experiments):
result: GateModelQuantumTaskResult = task.result()
counts: Dict[str,
int] = map_measurements(result.measurement_counts,
qasm_experiment)
data = ExperimentResultData(counts=dict(counts))
experiment_result = ExperimentResult(
shots=self.shots,
success=task.state() == 'COMPLETED',
header=qasm_experiment.header,
status=task.state(),
data=data)
experiment_results.append(experiment_result)
qiskit_result = Result(backend_name=self._backend.name(),
backend_version=self._backend.version(),
qobj_id=self._qobj.qobj_id,
job_id=self._job_id,
success=self.status(),
results=experiment_results)
return qiskit_result
def setUp(self):
experiment_result_data_1 = ExperimentResultData.from_dict(
{'counts': {
'0x0': 42,
'0x3': 58
}})
experiment_result_data_1.probabilities = {'0x0': 0.42, '0x3': 0.58}
experiment_result_data_2 = ExperimentResultData.from_dict(
{'counts': {
'0x0': 24,
'0x1': 25,
'0x2': 23,
'0x3': 28
}})
experiment_result_data_2.probabilities = {
'0x0': 0.24,
'0x1': 0.25,
'0x2': 0.23,
'0x3': 0.28
}
experiment_result_data_3 = ExperimentResultData.from_dict(
{'counts': {
'0x0': 24,
'0x1': 25,
'0x2': 23,
'0x3': 28
}})
header_1 = QobjHeader.from_dict({
'name': 'Test1',
'memory_slots': 2,
'creg_sizes': [['c0', 2]]
})
header_2 = QobjHeader.from_dict({
'name': 'Test2',
'memory_slots': 3,
'creg_sizes': [['c0', 3]]
})
header_3 = None
self.experiment_result_dictionary_1 = {
'name': 'Test1',
'shots': 42,
'data': experiment_result_data_1,
'status': 'DONE',
'success': True,
'time_taken': 0.42,
'header': header_1
}
self.experiment_result_dictionary_2 = {
'name': 'Test2',
'shots': 23,
'data': experiment_result_data_2,
'status': 'DONE',
'success': True,
'time_taken': 0.12,
'header': header_2
}
self.experiment_result_dictionary_3 = {
'name': 'Test3',
'shots': 23,
'data': experiment_result_data_3,
'status': 'DONE',
'success': True,
'time_taken': 0.12,
'header': header_3
}
self.experiment_result_1 = ExperimentResult(
**self.experiment_result_dictionary_1)
self.experiment_result_2 = ExperimentResult(
**self.experiment_result_dictionary_2)
self.experiment_result_3 = ExperimentResult(
**self.experiment_result_dictionary_3)
def setUp(self):
experiment_result_data_1 = ExperimentResultData.from_dict({
'counts': {
'0x0': 42,
'0x3': 58
},
'probabilities': {
'0x0': 0.42,
'0x3': 0.58
},
'calibration': {
'fridge_temperature': 26.9,
'unit': 'mK'
}
})
experiment_result_data_2 = ExperimentResultData.from_dict({
'counts': {
'0x0': 24,
'0x1': 25,
'0x2': 23,
'0x3': 28
},
'probabilities': {
'0x0': 0.24,
'0x1': 0.25,
'0x2': 0.23,
'0x3': 0.28
},
'calibration': {
'fridge_temperature': 25.0,
'unit': 'mK'
}
})
experiment_result_data_3 = ExperimentResultData.from_dict({
'counts': {
'0x0': 24,
'0x1': 25,
'0x2': 23,
'0x3': 28
},
'calibration':
None
})
experiment_result_data_4 = ExperimentResultData.from_dict(
{'counts': {
'0x0': 24,
'0x1': 25,
'0x2': 23,
'0x3': 28
}})
header_1 = QobjHeader.from_dict({
'name': 'Test1',
'memory_slots': 2,
'creg_sizes': [['c0', 2]]
})
header_2 = QobjHeader.from_dict({
'name': 'Test2',
'memory_slots': 3,
'creg_sizes': [['c0', 3]]
})
header_3 = None
header_4 = None
self.experiment_result_dictionary_1 = {
'name': 'Test1',
'shots': 42,
'data': experiment_result_data_1,
'status': 'DONE',
'success': True,
'time_taken': 0.42,
'header': header_1
}
self.experiment_result_dictionary_2 = {
'name': 'Test2',
'shots': 23,
'data': experiment_result_data_2,
'status': 'DONE',
'success': True,
'time_taken': 0.12,
'header': header_2
}
self.experiment_result_dictionary_3 = {
'name': 'Test3',
'shots': 23,
'data': experiment_result_data_3,
'status': 'DONE',
'success': True,
'time_taken': 0.12,
'header': header_3
}
self.experiment_result_dictionary_4 = {
'name': 'Test4',
'shots': 21,
'data': experiment_result_data_4,
'status': 'DONE',
'success': True,
'time_taken': 0.12,
'header': header_4
}
self.experiment_result_1 = ExperimentResult(
**self.experiment_result_dictionary_1)
self.experiment_result_2 = ExperimentResult(
**self.experiment_result_dictionary_2)
self.experiment_result_3 = ExperimentResult(
**self.experiment_result_dictionary_3)
self.experiment_result_4 = ExperimentResult(
**self.experiment_result_dictionary_4)
def run_experiment(self, experiment):
"""Run an experiment (circuit) and return a single experiment result.
Args:
experiment (QobjExperiment): experiment from qobj experiments list
Returns:
dict: A dictionary of results.
dict: A result dictionary
Raises:
QCGPUSimulatorError: If the number of qubits is too large, or another
error occurs during execution.
"""
self._number_of_qubits = experiment.header.n_qubits
self._statevector = 0
start = time.time()
try:
sim = qcgpu.State(self._number_of_qubits)
except OverflowError:
raise QCGPUSimulatorError('too many qubits')
for operation in experiment.instructions:
name = operation.name
qubits = operation.qubits
params = [
float(param) for param in getattr(operation, 'params', [])
]
if name == 'id':
logger.info('Identity gates are ignored.')
elif name == 'barrier':
logger.info('Barrier gates are ignored.')
elif name == 'u3':
sim.u(qubits[0], *params)
elif name == 'u2':
sim.u2(qubits[0], *params)
elif name == 'u1':
sim.u1(qubits[0], *params)
elif name == 'cx':
sim.cx(*qubits)
elif name == 'h':
sim.h(qubits[0])
elif name == 'x':
sim.x(qubits[0])
elif name == 'y':
sim.y(qubits[0])
elif name == 'z':
sim.z(qubits[0])
elif name == 's':
sim.s(qubits[0])
elif name == 't':
sim.t(qubits[0])
amps = [
complex(z) for z in sim.amplitudes().round(self._chop_threshold)
]
end = time.time()
# amps = np.stack((amps.real, amps.imag), axis=-1)
return ExperimentResult(shots=1,
success=True,
data=ExperimentResultData(statevector=amps),
time_taken=(end - start))
def setUp(self):
"""Setup variables used for testing."""
self.base_result_args = dict(
backend_name="test_backend",
backend_version="1.0.0",
qobj_id="id-123",
job_id="job-123",
success=True,
)
mem1 = ExperimentResultData(memory=[
[[1103260.0, -11378508.0], [2959012.0, -16488753.0]],
[[442170.0, -19283206.0], [-5279410.0, -15339630.0]],
[[3016514.0, -14548009.0], [-3404756.0, -16743348.0]],
])
mem2 = ExperimentResultData(memory=[
[[5131962.0, -16630257.0], [4438870.0, -13752518.0]],
[[3415985.0, -16031913.0], [2942458.0, -15840465.0]],
[[5199964.0, -14955998.0], [4030843.0, -14538923.0]],
])
header1 = QobjExperimentHeader(
clbit_labels=[["meas", 0], ["meas", 1]],
creg_sizes=[["meas", 2]],
global_phase=0.0,
memory_slots=2,
metadata={
"experiment_type": "fake_test_experiment",
"x_values": 0.0
},
)
header2 = QobjExperimentHeader(
clbit_labels=[["meas", 0], ["meas", 1]],
creg_sizes=[["meas", 2]],
global_phase=0.0,
memory_slots=2,
metadata={
"experiment_type": "fake_test_experiment",
"x_values": 1.0
},
)
res1 = ExperimentResult(shots=3,
success=True,
meas_level=1,
data=mem1,
header=header1)
res2 = ExperimentResult(shots=3,
success=True,
meas_level=1,
data=mem2,
header=header2)
self.result_lvl1 = Result(results=[res1, res2],
**self.base_result_args)
raw_counts = {"0x0": 4, "0x2": 6}
data = ExperimentResultData(counts=dict(**raw_counts))
header = QobjExperimentHeader(
metadata={"experiment_type": "fake_test_experiment"},
clbit_labels=[["c", 0], ["c", 1]],
creg_sizes=[["c", 2]],
n_qubits=2,
memory_slots=2,
)
res = ExperimentResult(shots=9,
success=True,
meas_level=2,
data=data,
header=header)
self.exp_data_lvl2 = ExperimentData(FakeExperiment())
self.exp_data_lvl2.add_data(
Result(results=[res], **self.base_result_args))
super().setUp()
def _process_job_result(self, job_result:Result, batch:Batch) -> Dict[str, Result]:
"""Post-process the job result corresponding to a batch. Recreate the single results by adding up the shots of multiple executions
Args:
job_result (Result)
batch (Batch): corresponding batch to the job_result
Returns:
Dict[str, Result]: Maps the keys of the initial QuantumExecutionJobs to their Results
"""
results = {}
exp_number = 0
# get the Result as dict and delete the results
result_dict = job_result.to_dict()
index = batch.batch_number
backend_name = batch.backend_name
try:
previous_key = self._previous_key[backend_name]
previous_memory = self._previous_memory[backend_name]
previous_counts = self._previous_counts[backend_name]
except KeyError:
previous_key = None
previous_memory = None
previous_counts = None
self._log.info(f"Process result of job {index}")
for exp in batch.experiments:
key = exp["key"]
circ = exp["circuit"]
reps = exp["reps"]
shots = exp["shots"]
total_shots = exp["total_shots"]
memory = []
counts = {}
result_data = None
if previous_memory:
# there is data from the previous job
assert(previous_key==key)
memory.extend(previous_memory)
counts.update(previous_counts)
shots += len(previous_memory)
total_shots += len(previous_memory)
previous_memory = None
previous_counts = None
previous_key = None
# get ExperimentResult as dict
job_exp_result_dict = job_result._get_experiment(exp_number).to_dict()
if not (shots == total_shots and reps == 1 and len(memory) == 0):
# do not run this block if it is only one experiment (shots == total_shots) with one repetition and no previous data is available
for exp_index in range(exp_number, exp_number+reps):
mem = job_result.data(exp_index)['memory']
memory.extend(mem)
cnts = job_result.data(exp_index)['counts']
if exp_index == exp_number+reps-1 and shots == total_shots:
# last experiment for this circuit
if len(memory) > total_shots:
# trim memory and counts w.r.t. number of shots
too_much = len(memory) - total_shots
memory = memory[:total_shots]
mem = mem[:-too_much]
cnts = dict(Counter(mem))
counts = self._add_dicts(counts, cnts)
if shots < total_shots:
previous_memory = copy.deepcopy(memory)
previous_counts = copy.deepcopy(counts)
previous_key = key
continue
if self._memory:
result_data = ExperimentResultData(counts=counts, memory=memory).to_dict()
else:
result_data = ExperimentResultData(counts=counts).to_dict()
# overwrite the data and the shots
job_exp_result_dict["data"] = result_data
job_exp_result_dict["shots"] = total_shots
else:
if not self._memory:
counts = job_result.data(exp_number)['counts']
result_data = ExperimentResultData(counts=counts).to_dict()
job_exp_result_dict["data"] = result_data
# overwrite the results with the computed result
result_dict["results"] = [job_exp_result_dict]
results[key] = Result.from_dict(result_dict)
exp_number += reps
self._previous_key[backend_name] = previous_key
self._previous_memory[backend_name] = previous_memory
self._previous_counts[backend_name] = previous_counts
return results
def qiskit_experiment_result_from_statevector(sv) -> ExperimentResult:
experiment_data = ExperimentResultData(statevector=sv)
experiment_result = ExperimentResult(shots=1,
success=True,
data=experiment_data)
return experiment_result
