def probability(target: QubitSetInput = None) -> ResultType:
"""Registers this function into the circuit class.
Args:
target (int, Qubit, or iterable of int / Qubit, optional): The target qubits that the
result type is requested for. Default is `None`, which means all qubits for the
circuit.
Returns:
ResultType: probability as a requested result type
Examples:
>>> circ = Circuit().probability(target=[0, 1])
"""
return ResultType.Probability(target=target)
def get_probabilities(
self,
circuit: Circuit,
qubits: Union[Iterable[int], None] = None,
n_shots: int = 0,
valid_check: bool = True,
**kwargs: KwargTypes,
) -> np.ndarray:
"""
Compute the (exact or empirical) probability distribution of outcomes.
If `n_shots > 0` the probabilities are calculated empirically by measurements.
If `n_shots = 0` (if supported) they are calculated exactly by simulation.
Supported `kwargs` are as for `BraketBackend.process_circuits`.
The order is big-endian with respect to the order of qubits in the argument.
For example, if qubits=[0,1] then the order of probabilities is [p(0,0), p(0,1),
p(1,0), p(1,1)], while if qubits=[1,0] the order is [p(0,0), p(1,0), p(0,1),
p(1,1)], where p(i,j) is the probability of qubit 0 being in state i and qubit 1
being in state j.
:param qubits: qubits of interest
:returns: list of probabilities of outcomes if initial state is all-zeros
"""
if not self.supports_probability:
raise RuntimeError("Backend does not support probability")
if (n_shots < self._probability_min_shots
or n_shots > self._probability_max_shots):
raise ValueError(
f"n_shots must be between {self._probability_min_shots} and "
f"{self._probability_max_shots}")
if valid_check:
self._check_all_circuits([circuit], nomeasure_warn=False)
bkcirc = self._to_bkcirc(circuit)
restype = ResultType.Probability(target=qubits)
bkcirc.add_result_type(restype)
task = self._run(bkcirc, n_shots=n_shots, **kwargs)
res = task.result()
return res.get_value_by_result_type(restype) # type: ignore
