def __init__(self, matrix: np.ndarray, targets: Optional[List[int]] = None):
clone = np.array(matrix, dtype=complex)
self._targets = tuple(targets) if targets else None
if targets:
check_matrix_dimensions(clone, self._targets)
elif clone.shape != (2, 2):
raise ValueError(
f"Matrix must have shape (2, 2) if target is empty, but has shape {clone.shape}"
)
check_hermitian(clone)
self._matrix = clone
def __init__(self,
matrix: np.ndarray,
targets: Optional[List[int]] = None):
clone = np.array(matrix, dtype=complex)
self._targets = tuple(targets) if targets else None
if targets:
check_matrix_dimensions(clone, self._targets)
elif clone.shape != (2, 2):
raise ValueError(
f"Matrix must have shape (2, 2) if target is empty, but has shape {clone.shape}"
)
check_hermitian(clone)
self._matrix = clone
eigendecomposition = Hermitian._eigendecomposition(clone)
self._eigenvalues = eigendecomposition["eigenvalues"]
self._diagonalizing_matrix = eigendecomposition["eigenvectors"].conj(
).T
def __init__(self, targets, matrix):
self._targets = tuple(targets)
clone = np.array(matrix, dtype=complex)
check_matrix_dimensions(clone, self._targets)
check_unitary(clone)
self._matrix = clone
def test_check_matrix_dimensions_invalid_matrix(matrix):
check_matrix_dimensions(matrix, (0, ))