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 _compute_samples_display_value(display: ops.SamplesDisplay,
state: np.ndarray,
qubit_order: ops.QubitOrder,
qubit_map: Dict[ops.Qid, int]):
basis_change_circuit = circuits.Circuit(display.measurement_basis_change())
modified_state = basis_change_circuit.final_wavefunction(
state,
qubit_order=qubit_order,
qubits_that_should_be_present=qubit_map.keys())
indices = [qubit_map[qubit] for qubit in display.qubits]
samples = wave_function.sample_state_vector(
modified_state, indices, repetitions=display.num_samples)
return display.value_derived_from_samples(samples)