def run_sweep(
self,
job_id: Optional[str] = None,
params: cirq.Sweepable = None,
repetitions: int = 1,
processor_ids: Sequence[str] = ('xmonsim', ),
description: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
) -> engine_job.EngineJob:
"""Runs the program on the QuantumEngine.
In contrast to run, this runs across multiple parameter sweeps, and
does not block until a result is returned.
Args:
job_id: Optional job id to use. If this is not provided, a random id
of the format 'job-################YYMMDD' will be generated,
where # is alphanumeric and YYMMDD is the current year, month,
and day.
params: Parameters to run with the program.
repetitions: The number of circuit repetitions to run.
processor_ids: The engine processors that should be candidates
to run the program. Only one of these will be scheduled for
execution.
description: An optional description to set on the job.
labels: Optional set of labels to set on the job.
Returns:
An EngineJob. If this is iterated over it returns a list of
TrialResults, one for each parameter sweep.
Raises:
ValueError: If called on a program that is a batch of programs.
"""
import cirq_google.engine.engine as engine_base
if self.result_type != ResultType.Program:
raise ValueError('Please use run_batch() for batch mode.')
if not job_id:
job_id = engine_base._make_random_id('job-')
sweeps = cirq.to_sweeps(params or cirq.ParamResolver({}))
run_context = self._serialize_run_context(sweeps, repetitions)
created_job_id, job = self.context.client.create_job(
project_id=self.project_id,
program_id=self.program_id,
job_id=job_id,
processor_ids=processor_ids,
run_context=run_context,
description=description,
labels=labels,
)
return engine_job.EngineJob(self.project_id, self.program_id,
created_job_id, self.context, job)
def run_context_to_proto(
sweepable: cirq.Sweepable, repetitions: int, *, out: Optional[run_context_pb2.RunContext] = None
) -> run_context_pb2.RunContext:
"""Populates a RunContext protobuf message.
Args:
sweepable: The sweepable to include in the run context.
repetitions: The number of repetitions for the run context.
out: Optional message to be populated. If not given, a new message will
be created.
Returns:
Populated RunContext protobuf message.
"""
if out is None:
out = run_context_pb2.RunContext()
for sweep in cirq.to_sweeps(sweepable):
sweep_proto = out.parameter_sweeps.add()
sweep_proto.repetitions = repetitions
sweep_to_proto(sweep, out=sweep_proto.sweep)
return out
def run_batch(
self,
job_id: Optional[str] = None,
params_list: List[cirq.Sweepable] = None,
repetitions: int = 1,
processor_ids: Sequence[str] = (),
description: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
) -> engine_job.EngineJob:
"""Runs a batch of circuits on the QuantumEngine.
This method should only be used if the Program object was created
with a BatchProgram. The number of parameter sweeps should match
the number of circuits within that BatchProgram.
This method does not block until a result is returned. However,
no results will be available until the entire batch is complete.
Args:
job_id: Optional job id to use. If this is not provided, a random id
of the format 'job-################YYMMDD' will be generated,
where # is alphanumeric and YYMMDD is the current year, month,
and day.
params_list: Parameter sweeps to run with the program. There must
be one Sweepable object for each circuit in the batch. If this
is None, it is assumed that the circuits are not parameterized
and do not require sweeps.
repetitions: The number of circuit repetitions to run.
processor_ids: The engine processors that should be candidates
to run the program. Only one of these will be scheduled for
execution.
description: An optional description to set on the job.
labels: Optional set of labels to set on the job.
Returns:
An EngineJob. If this is iterated over it returns a list of
TrialResults. All TrialResults for the first circuit are listed
first, then the TrialResults for the second, etc. The TrialResults
for a circuit are listed in the order imposed by the associated
parameter sweep.
Raises:
ValueError: if the program was not a batch program or no processors
were supplied.
"""
import cirq_google.engine.engine as engine_base
if self.result_type != ResultType.Batch:
raise ValueError('Can only use run_batch() in batch mode.')
if params_list is None:
params_list = [None] * self.batch_size()
if not job_id:
job_id = engine_base._make_random_id('job-')
if not processor_ids:
raise ValueError('No processors specified')
# Pack the run contexts into batches
batch = v2.batch_pb2.BatchRunContext()
for param in params_list:
sweeps = cirq.to_sweeps(param)
current_context = batch.run_contexts.add()
for sweep in sweeps:
sweep_proto = current_context.parameter_sweeps.add()
sweep_proto.repetitions = repetitions
v2.sweep_to_proto(sweep, out=sweep_proto.sweep)
batch_context = qtypes.any_pb2.Any()
batch_context.Pack(batch)
created_job_id, job = self.context.client.create_job(
project_id=self.project_id,
program_id=self.program_id,
job_id=job_id,
processor_ids=processor_ids,
run_context=batch_context,
description=description,
labels=labels,
)
return engine_job.EngineJob(
self.project_id,
self.program_id,
created_job_id,
self.context,
job,
result_type=ResultType.Batch,
)