def test_qwc_complement_adj_matrix_exception(self):
"""Tests that the ``qwc_complement_adj_matrix`` function raises an exception if
the matrix is not binary."""
not_binary_observables = np.array([[1.1, 0.5, 1., 0., 0., 1.],
[0., 1.3, 1., 1., 0., 1.],
[2.2, 0., 0., 1., 0., 0.]])
with pytest.raises(ValueError,
match="Expected a binary array, instead got"):
qwc_complement_adj_matrix(not_binary_observables)
def test_qwc_complement_adj_matrix(self):
"""Tests that the ``qwc_complement_adj_matrix`` function returns the correct
adjacency matrix."""
binary_observables = np.array([[1., 0., 1., 0., 0., 1.],
[0., 1., 1., 1., 0., 1.],
[0., 0., 0., 1., 0., 0.]])
adj = qwc_complement_adj_matrix(binary_observables)
expected = np.array([[0., 1., 1.], [1., 0., 0.], [1., 0., 0.]])
assert np.all(adj == expected)
binary_obs_list = list(binary_observables)
adj = qwc_complement_adj_matrix(binary_obs_list)
assert np.all(adj == expected)
binary_obs_tuple = tuple(binary_observables)
adj = qwc_complement_adj_matrix(binary_obs_tuple)
assert np.all(adj == expected)
def complement_adj_matrix_for_operator(self):
"""Constructs the adjacency matrix for the complement of the Pauli graph.
The adjacency matrix for an undirected graph of N vertices is an N by N symmetric binary
matrix, where matrix elements of 1 denote an edge, and matrix elements of 0 denote no edge.
Returns:
array[int]: the square and symmetric adjacency matrix
"""
if self.binary_observables is None:
self.binary_observables = self.binary_repr()
n_qubits = int(np.shape(self.binary_observables)[1] / 2)
if self.grouping_type == "qwc":
adj = qwc_complement_adj_matrix(self.binary_observables)
elif self.grouping_type in frozenset(["commuting", "anticommuting"]):
symplectic_form = np.block(
[
[np.zeros((n_qubits, n_qubits)), np.eye(n_qubits)],
[np.eye(n_qubits), np.zeros((n_qubits, n_qubits))],
]
)
mat_prod = (
self.binary_observables @ symplectic_form @ np.transpose(self.binary_observables)
)
if self.grouping_type == "commuting":
adj = mat_prod % 2
elif self.grouping_type == "anticommuting":
adj = (mat_prod + 1) % 2
np.fill_diagonal(adj, 0)
return adj