def __init__(self,
matrices: Iterable[np.ndarray],
display_name: str = "KR"):
for matrix in matrices:
verify_quantum_operator_matrix_dimensions(matrix)
if not int(np.log2(matrix.shape[0])) == int(
np.log2(matrices[0].shape[0])):
raise ValueError(
f"all matrices in {matrices} must have the same shape")
self._matrices = [
np.array(matrix, dtype=complex) for matrix in matrices
]
self._display_name = display_name
qubit_count = int(np.log2(self._matrices[0].shape[0]))
if qubit_count > 2:
raise ValueError(
"Kraus operators with more than two qubits are not supported.")
if len(matrices) > 2**(2 * qubit_count):
raise ValueError("The number of Kraus operators is beyond limit.")
if not is_cptp(self._matrices):
raise ValueError(
"The input matrices do not define a completely-positive trace-preserving map."
)
super().__init__(qubit_count=qubit_count,
ascii_symbols=[display_name] * qubit_count)
def __init__(self, matrix: np.ndarray, display_name: str = "Hermitian"):
"""
Args:
matrix (numpy.ndarray): Hermitian matrix that defines the observable.
display_name (str): Name to use for an instance of this Hermitian matrix
observable for circuit diagrams. Defaults to `Hermitian`.
Raises:
ValueError: If `matrix` is not a two-dimensional square matrix,
or has a dimension length that is not a positive power of 2,
or is not Hermitian.
Examples:
>>> Observable.Hermitian(matrix=np.array([[0, 1],[1, 0]]))
"""
verify_quantum_operator_matrix_dimensions(matrix)
self._matrix = np.array(matrix, dtype=complex)
if not is_hermitian(self._matrix):
raise ValueError(f"{self._matrix} is not hermitian")
qubit_count = int(np.log2(self._matrix.shape[0]))
eigendecomposition = Hermitian._get_eigendecomposition(self._matrix)
self._eigenvalues = eigendecomposition["eigenvalues"]
self._diagonalizing_gates = (Gate.Unitary(
matrix=eigendecomposition["eigenvectors"].conj().T), )
super().__init__(qubit_count=qubit_count,
ascii_symbols=[display_name] * qubit_count)
def __init__(self, matrix: np.ndarray, display_name: str = "U"):
verify_quantum_operator_matrix_dimensions(matrix)
self._matrix = np.array(matrix, dtype=complex)
qubit_count = int(np.log2(self._matrix.shape[0]))
if not is_unitary(self._matrix):
raise ValueError(f"{self._matrix} is not unitary")
super().__init__(qubit_count=qubit_count, ascii_symbols=[display_name] * qubit_count)
def test_verify_quantum_operator_matrix_dimensions_value_error(matrix):
verify_quantum_operator_matrix_dimensions(matrix)
def test_verify_quantum_operator_matrix_dimensions():
assert verify_quantum_operator_matrix_dimensions(
valid_unitary_hermitian_matrix) is None