def _apply_unitary_(self, args: cirq.ApplyUnitaryArgs
) -> Optional[np.ndarray]:
if cirq.is_parameterized(self):
return None
am = cirq.unitary(cirq.Rx(-np.pi * self.exponent * self.weights[0]))
bm = cirq.unitary(cirq.Rx(-np.pi * self.exponent * self.weights[1]))
cm = cirq.unitary(cirq.Rx(-np.pi * self.exponent * self.weights[2]))
a1 = args.subspace_index(0b1001)
b1 = args.subspace_index(0b0101)
c1 = args.subspace_index(0b0011)
a2 = args.subspace_index(0b0110)
b2 = args.subspace_index(0b1010)
c2 = args.subspace_index(0b1100)
cirq.apply_matrix_to_slices(args.target_tensor,
am,
slices=[a1, a2],
out=args.available_buffer)
cirq.apply_matrix_to_slices(args.available_buffer,
bm,
slices=[b1, b2],
out=args.target_tensor)
return cirq.apply_matrix_to_slices(args.target_tensor,
cm,
slices=[c1, c2],
out=args.available_buffer)
def _apply_unitary_(self,
args: cirq.ApplyUnitaryArgs) -> Optional[np.ndarray]:
if cirq.is_parameterized(self):
return NotImplemented
am, bm, cm = (la.expm(-1j * self.exponent *
np.array([[0, w], [w.conjugate(), 0]]))
for w in self.weights)
a1 = args.subspace_index(0b1001)
b1 = args.subspace_index(0b0101)
c1 = args.subspace_index(0b0011)
a2 = args.subspace_index(0b0110)
b2 = args.subspace_index(0b1010)
c2 = args.subspace_index(0b1100)
cirq.apply_matrix_to_slices(args.target_tensor,
am,
slices=[a1, a2],
out=args.available_buffer)
cirq.apply_matrix_to_slices(args.available_buffer,
bm,
slices=[b1, b2],
out=args.target_tensor)
return cirq.apply_matrix_to_slices(args.target_tensor,
cm,
slices=[c1, c2],
out=args.available_buffer)
def _apply_unitary_(self,
args: cirq.ApplyUnitaryArgs) -> Optional[np.ndarray]:
if self.exponent != 1:
return None
oi = args.subspace_index(0b01)
io = args.subspace_index(0b10)
ii = args.subspace_index(0b11)
args.available_buffer[oi] = args.target_tensor[oi]
args.target_tensor[oi] = args.target_tensor[io]
args.target_tensor[io] = args.available_buffer[oi]
args.target_tensor[ii] *= -1
return args.target_tensor
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:
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