def _apply_unitary_(
self, args: cirq.ApplyUnitaryArgs
) -> Union[np.ndarray, NotImplementedType]:
if self.exponent != 1 or cirq.is_parameterized(self):
# coverage: ignore
return NotImplemented
zero = cirq.slice_for_qubits_equal_to(args.axes, 0)
one = cirq.slice_for_qubits_equal_to(args.axes, 1)
c = np.exp(1j * np.pi * self.phase_exponent)
args.target_tensor[one] *= c
args.available_buffer[zero] = args.target_tensor[one]
args.available_buffer[one] = args.target_tensor[zero]
args.available_buffer[one] *= c
return args.available_buffer
def _apply_unitary_(self, args: cirq.ApplyUnitaryArgs
) -> Optional[np.ndarray]:
if cirq.is_parameterized(self):
return None
inner_matrix = cirq.unitary(cirq.rx(-2 * np.pi * self.exponent))
a = args.subspace_index(0b0011)
b = args.subspace_index(0b1100)
return cirq.apply_matrix_to_slices(args.target_tensor,
inner_matrix,
slices=[a, b],
out=args.available_buffer)
def _apply_unitary_(self,
args: cirq.ApplyUnitaryArgs) -> Optional[np.ndarray]:
if cirq.is_parameterized(self):
return None
inner_matrix = cirq.unitary(cirq.Ry(-self.exponent * np.pi))
oi = args.subspace_index(0b01)
io = args.subspace_index(0b10)
return cirq.apply_matrix_to_slices(args.target_tensor,
inner_matrix,
slices=[oi, io],
out=args.available_buffer)
def _apply_unitary_(self, args: cirq.ApplyUnitaryArgs) -> np.ndarray:
i = args.subspace_index(1)
args.target_tensor[i] *= self.power * 2
return args.target_tensor
def _apply_unitary_(self, args: cirq.ApplyUnitaryArgs) -> np.ndarray:
o = args.subspace_index(0)
i = args.subspace_index(1)
args.available_buffer[o] = args.target_tensor[i]
args.available_buffer[i] = args.target_tensor[o]
return args.available_buffer
def _apply_unitary_(self, args: cirq.ApplyUnitaryArgs) -> np.ndarray:
args.available_buffer[0] = args.target_tensor[1]
args.available_buffer[1] = args.target_tensor[0]
return args.available_buffer
def _apply_unitary_(self, args: cirq.ApplyUnitaryArgs) -> np.ndarray:
one = args.subspace_index(1)
args.target_tensor[one] *= -1
return args.target_tensor
def _apply_unitary_(self, args: cirq.ApplyUnitaryArgs) -> np.ndarray:
zero = args.subspace_index(0)
one = args.subspace_index(1)
args.available_buffer[zero] = args.target_tensor[zero]
args.available_buffer[one] = -args.target_tensor[one]
return args.available_buffer
def _apply_unitary_(self, args: cirq.ApplyUnitaryArgs):
args.target_tensor[0], args.target_tensor[1] = (
args.target_tensor[1],
args.target_tensor[0],
)
return args.target_tensor