def test_kraus_to_super():
"""Tests the function on random channels acting on random states.
Channels and states are non-physical, but this is irrelevant for the test.
"""
for num_qubits in (1, 2, 3, 4, 5):
d = 2**num_qubits
fake_kraus_ops = [
np.random.rand(d, d) + 1.0j * np.random.rand(d, d)
for _ in range(7)
]
super_op = kraus_to_super(fake_kraus_ops)
fake_state = np.random.rand(d, d) + 1.0j * np.random.rand(d, d)
result_with_kraus = sum(
[k @ fake_state @ k.conj().T for k in fake_kraus_ops])
result_with_super = vector_to_matrix(
super_op @ matrix_to_vector(fake_state))
assert np.allclose(result_with_kraus, result_with_super)
def test_vector_to_matrix():
for d in [1, 2, 3, 4]:
vec = np.random.rand(d**2)
assert vector_to_matrix(vec).shape == (d, d)
assert (matrix_to_vector(vector_to_matrix(vec)) == vec).all
def test_non_squared_dimension():
with raises(ValueError, match="must be a square number"):
vector_to_matrix(np.random.rand(7))
with raises(ValueError, match="must be a square number"):
choi_to_super(np.random.rand(7, 7))
def test_matrix_to_vector():
for d in [1, 2, 3, 4]:
mat = np.random.rand(d, d)
assert matrix_to_vector(mat).shape == (d**2, )
assert (vector_to_matrix(matrix_to_vector(mat)) == mat).all
