def compute_displays_sweep(
self,
program: Union[circuits.Circuit, schedules.Schedule],
params: Optional[study.Sweepable] = None,
qubit_order: ops.QubitOrderOrList = ops.QubitOrder.DEFAULT,
initial_state: Union[int, np.ndarray] = 0,
) -> List[study.ComputeDisplaysResult]:
"""Computes displays in the supplied Circuit or Schedule.
In contrast to `compute_displays`, this allows for sweeping
over different parameter values.
Args:
program: The circuit or schedule to simulate.
params: Parameters to run with the program.
qubit_order: Determines the canonical ordering of the qubits used to
define the order of amplitudes in the wave function.
initial_state: If an int, the state is set to the computational
basis state corresponding to this state. Otherwise if it is a
np.ndarray it is the full initial state, either a pure state
or the full density matrix. If it is the pure state it must be
the correct size, be normalized (an L2 norm of 1), and be
safely castable to an appropriate dtype for the simulator.
If it is a mixed state it must be correctly sized and
positive semidefinite with trace one.
Returns:
List of ComputeDisplaysResults for this run, one for each
possible parameter resolver.
"""
circuit = (program.to_circuit()
if isinstance(program, schedules.Schedule) else program)
qubit_order = ops.QubitOrder.as_qubit_order(qubit_order)
qubits = qubit_order.order_for(circuit.all_qubits())
compute_displays_results: List[study.ComputeDisplaysResult] = []
for param_resolver in study.to_resolvers(params):
display_values: Dict[Hashable, Any] = {}
# Compute the displays in the first Moment
moment = circuit[0]
matrix = density_matrix_utils.to_valid_density_matrix(
initial_state, num_qubits=len(qubits), dtype=self._dtype)
qubit_map = {q: i for i, q in enumerate(qubits)}
_enter_moment_display_values_into_dictionary(
display_values, moment, matrix, qubits, qubit_map)
# Compute the displays in the rest of the Moments
all_step_results = self.simulate_moment_steps(
circuit, param_resolver, qubits, initial_state)
for step_result, moment in zip(all_step_results, circuit[1:]):
_enter_moment_display_values_into_dictionary(
display_values, moment, step_result.density_matrix(),
qubits, step_result._qubit_map)
compute_displays_results.append(
study.ComputeDisplaysResult(params=param_resolver,
display_values=display_values))
return compute_displays_results
def compute_samples_displays_sweep(
self,
program: Union[circuits.Circuit, schedules.Schedule],
params: Optional[study.Sweepable] = None
) -> List[study.ComputeDisplaysResult]:
"""Computes SamplesDisplays in the supplied Circuit or Schedule.
In contrast to `compute_displays`, this allows for sweeping
over different parameter values.
Args:
program: The circuit or schedule to simulate.
params: Parameters to run with the program.
Returns:
List of ComputeDisplaysResults for this run, one for each
possible parameter resolver.
"""
circuit = (program if isinstance(program, circuits.Circuit)
else program.to_circuit())
param_resolvers = study.to_resolvers(params or study.ParamResolver({}))
compute_displays_results = [] # type: List[study.ComputeDisplaysResult]
for param_resolver in param_resolvers:
display_values = {} # type: Dict[Hashable, Any]
preceding_circuit = circuits.Circuit()
for i, moment in enumerate(circuit):
displays = (op for op in moment
if isinstance(op, ops.SamplesDisplay))
for display in displays:
measurement_key = str(display.key)
measurement_circuit = circuits.Circuit.from_ops(
display.measurement_basis_change(),
ops.measure(*display.qubits,
key=measurement_key)
)
measurements = self._run(
preceding_circuit + measurement_circuit,
param_resolver,
display.num_samples)
display_values[display.key] = (
display.value_derived_from_samples(
measurements[measurement_key]))
preceding_circuit.append(circuit[i])
compute_displays_results.append(study.ComputeDisplaysResult(
params=param_resolver,
display_values=display_values))
return compute_displays_results
