def _eigen_components(self):
return [
(0, linalg.block_diag(np.diag([1, 1, 1, 1, 1, 1]),
np.array([[0.5, 0.5], [0.5, 0.5]]))),
(1, linalg.block_diag(np.diag([0, 0, 0, 0, 0, 0]),
np.array([[0.5, -0.5], [-0.5, 0.5]]))),
]
def _eigen_components(self) -> List[Tuple[float, np.ndarray]]:
return [
(0, linalg.block_diag(np.diag([1, 1, 1, 1, 1, 1]), np.array([[0.5, 0.5], [0.5, 0.5]]))),
(
1,
linalg.block_diag(
np.diag([0, 0, 0, 0, 0, 0]), np.array([[0.5, -0.5], [-0.5, 0.5]])
),
),
]
def _unitary_(self) -> Union[np.ndarray, NotImplementedType]:
sub_matrix = protocols.unitary(self.sub_gate, None)
if sub_matrix is None:
return NotImplemented
return linalg.block_diag(
np.eye(pow(2, self.num_qubits()) - sub_matrix.shape[0]),
sub_matrix)
def _unitary_(self) -> np.ndarray:
return linalg.block_diag(np.diag([1, 1, 1, 1, 1]), np.array([[0, 1], [1, 0]]), np.diag([1]))
def _unitary_(self) -> Union[np.ndarray, type(NotImplemented)]:
sub_matrix = protocols.unitary(self.sub_gate, None)
if sub_matrix is None:
return NotImplemented
return linalg.block_diag(np.eye(sub_matrix.shape[0]), sub_matrix)
def matrix(self):
return linalg.block_diag(np.diag([1, 1, 1, 1, 1]),
np.array([[0, 1], [1, 0]]),
np.diag([1]))
def matrix(self) -> np.ndarray:
cast_sub_gate = self._cast_sub_gate(gate_features.KnownMatrixGate)
sub_matrix = cast_sub_gate.matrix()
return linalg.block_diag(np.eye(sub_matrix.shape[0]), sub_matrix)
def matrix(self):
return linalg.block_diag(np.diag([1, 1, 1, 1, 1]),
np.array([[0, 1], [1, 0]]),
np.diag([1]))
def matrix(self) -> np.ndarray:
cast_sub_gate = self._cast_sub_gate(gate_features.KnownMatrix)
sub_matrix = cast_sub_gate.matrix()
return linalg.block_diag(np.eye(sub_matrix.shape[0]), sub_matrix)
