def sample(
self,
qubits: List[ops.Qid],
repetitions: int = 1,
seed: 'cirq.RANDOM_STATE_OR_SEED_LIKE' = None,
) -> np.ndarray:
indices = [self._qubit_map[q] for q in qubits]
return density_matrix_utils.sample_density_matrix(
self._simulator_state().density_matrix,
indices,
qid_shape=self._qid_shape,
repetitions=repetitions,
seed=seed,
)
def _compute_samples_display_value(display: ops.SamplesDisplay,
state: np.ndarray,
qubit_order: ops.QubitOrder,
qubit_map: Dict[ops.Qid, int]):
n = len(qubit_map)
state = np.reshape(state, (2, ) * n * 2)
basis_change = ops.flatten_op_tree(display.measurement_basis_change())
for op in basis_change:
# TODO: Use apply_channel similar to apply_unitary.
indices = [qubit_map[qubit] for qubit in op.qubits]
gate = cast(ops.GateOperation, op).gate
unitary = protocols.unitary(gate)
krauss_tensor = np.reshape(unitary, (2, ) * gate.num_qubits() * 2)
state = linalg.targeted_left_multiply(krauss_tensor, state, indices)
# TODO add a test that fails if the below is not performed
state = linalg.targeted_left_multiply(np.conjugate(krauss_tensor),
state, [x + n for x in indices])
state = state.reshape((2**n, 2**n))
indices = [qubit_map[qubit] for qubit in display.qubits]
samples = density_matrix_utils.sample_density_matrix(
state, indices, display.num_samples)
return display.value_derived_from_samples(samples)
def sample(self,
qubits: List[ops.Qid],
repetitions: int = 1) -> np.ndarray:
indices = [self._qubit_map[q] for q in qubits]
return density_matrix_utils.sample_density_matrix(
self.simulator_state().density_matrix, indices, repetitions)
