def _apply_unitary_strat(
val: Any, args: 'ApplyMixtureArgs', is_density_matrix: bool
) -> Optional[np.ndarray]:
"""Attempt to use `apply_unitary` and return the result.
If `val` does not support `apply_unitary` returns None.
"""
left_args = ApplyUnitaryArgs(
target_tensor=args.target_tensor,
available_buffer=args.auxiliary_buffer0,
axes=args.left_axes,
)
left_result = apply_unitary(val, left_args, None)
if left_result is None:
return None
if not is_density_matrix:
return left_result
# cast is ok, is_density_matrix being false tells us right_axes isn't None.
right_args = ApplyUnitaryArgs(
target_tensor=np.conjugate(left_result),
available_buffer=args.auxiliary_buffer0,
axes=cast(Tuple[int], args.right_axes),
)
right_result = apply_unitary(val, right_args)
np.conjugate(right_result, out=right_result)
return right_result
def _apply_unitary(val: Any, args: 'ApplyChannelArgs') -> Optional[np.ndarray]:
"""Attempt to use `apply_unitary` and return the result.
If `val` does not support `apply_unitary` returns None.
"""
left_args = ApplyUnitaryArgs(target_tensor=args.target_tensor,
available_buffer=args.auxiliary_buffer0,
axes=args.left_axes)
left_result = apply_unitary(val, left_args, None)
if left_result is None:
return None
right_args = ApplyUnitaryArgs(target_tensor=np.conjugate(left_result),
available_buffer=args.out_buffer,
axes=args.right_axes)
right_result = apply_unitary(val, right_args)
np.conjugate(right_result, out=right_result)
return right_result
def _strat_has_unitary_from_apply_unitary(val: Any) -> Optional[bool]:
"""Attempts to infer a value's unitary-ness via its _apply_unitary_ method."""
method = getattr(val, '_apply_unitary_', None)
if method is None:
return None
val_qid_shape = qid_shape_protocol.qid_shape(val, None)
if val_qid_shape is None:
return None
state = qis.one_hot(shape=val_qid_shape, dtype=np.complex64)
buffer = np.empty_like(state)
result = method(ApplyUnitaryArgs(state, buffer, range(len(val_qid_shape))))
if result is NotImplemented:
return None
return result is not None
def _strat_unitary_from_decompose(val: Any) -> Optional[np.ndarray]:
"""Attempts to compute a value's unitary via its _decompose_ method."""
# Check if there's a decomposition.
operations, qubits, val_qid_shape = _try_decompose_into_operations_and_qubits(
val)
if operations is None:
return NotImplemented
# Apply sub-operations' unitary effects to an identity matrix.
state = qis.eye_tensor(val_qid_shape, dtype=np.complex128)
buffer = np.empty_like(state)
result = apply_unitaries(
operations, qubits,
ApplyUnitaryArgs(state, buffer, range(len(val_qid_shape))), None)
# Package result.
if result is None:
return None
state_len = np.prod(val_qid_shape, dtype=np.int64)
return result.reshape((state_len, state_len))
def _strat_unitary_from_apply_unitary(val: Any) -> Optional[np.ndarray]:
"""Attempts to compute a value's unitary via its _apply_unitary_ method."""
# Check for the magic method.
method = getattr(val, '_apply_unitary_', None)
if method is None:
return NotImplemented
# Get the qid_shape.
val_qid_shape = qid_shape_protocol.qid_shape(val, None)
if val_qid_shape is None:
return NotImplemented
# Apply unitary effect to an identity matrix.
state = qis.eye_tensor(val_qid_shape, dtype=np.complex128)
buffer = np.empty_like(state)
result = method(ApplyUnitaryArgs(state, buffer, range(len(val_qid_shape))))
if result is NotImplemented or result is None:
return result
state_len = np.prod(val_qid_shape, dtype=np.int64)
return result.reshape((state_len, state_len))