def test_choi_is_completely_positive():
D = 2
K = 2
choi = rand_ops.rand_map_with_BCSZ_dist(D, K)
assert choi_is_completely_positive(choi)
D = 3
K = 2
choi = rand_ops.rand_map_with_BCSZ_dist(D, K)
assert choi_is_completely_positive(choi)
def test_BCSZ_dist_is_trace_preserving():
D = 2
K = 2
choi = rand_ops.rand_map_with_BCSZ_dist(D, K)
choi_tensor = choi.reshape([D, D, D, D])
choi_red = np.trace(choi_tensor, axis1=0, axis2=2)
assert np.isclose(choi_red, np.eye(D)).all()
def test_BCSZ_dist_is_complete_positive():
# A quantum channel is completely positive, iff the Choi matrix is non-negative.
D = 2
K = 2
for idx in range(0, 10):
choi = rand_ops.rand_map_with_BCSZ_dist(D, K)
assert choi_is_completely_positive(choi)
def test_BCSZ_dist_is_complete_positive():
# A quantum channel is completely positive, iff the Choi matrix is non-negative.
D = 2
K = 2
N_avg = 10
eigenvallist = []
for idx in range(0, N_avg):
choi = rand_ops.rand_map_with_BCSZ_dist(D, K)
eigenval = la.eig(choi)[0]
eigenvallist += [eigenval]
eigenvalues = np.asarray(eigenvallist)
eigenvalues = eigenvalues.reshape(1, D * D * N_avg)
assert np.max(np.absolute(np.imag(eigenvalues))) < 1e-10
assert np.min(np.real(eigenvalues)) >= -1e-10
def test_choi_is_trace_preserving():
D = 2
K = 2
choi = rand_ops.rand_map_with_BCSZ_dist(D, K)
assert choi_is_trace_preserving(choi)