def test_dnorm_force_solve(self, dim, matrix_generator):
"""
Metrics: checks that special cases for dnorm agree with SDP solutions.
"""
if matrix_generator == 'bcsz':
A = rand_super_bcsz(dim)
B = rand_super_bcsz(dim)
elif matrix_generator == 'haar':
A = rand_unitary_haar(dim)
B = rand_unitary_haar(dim)
assert (dnorm(A, B, force_solve=False) == pytest.approx(dnorm(
A, B, force_solve=True),
abs=1e-5))
def test_dnorm_force_solve():
"""
Metrics: checks that special cases for dnorm agree with SDP solutions.
"""
def case(A, B, significant=4):
assert_approx_equal(dnorm(A, B, force_solve=False),
dnorm(A, B, force_solve=True))
for dim in (2, 3):
for _ in range(10):
yield (case, rand_super_bcsz(dim), None)
for _ in range(10):
yield (case, rand_unitary_haar(dim), rand_unitary_haar(dim))
def test_rand_unitary_haar_unitarity():
"""
Random Qobjs: Tests that unitaries are actually unitary.
"""
U = rand_unitary_haar(5)
I = qeye(5)
assert_(U * U.dag() == I)
def test_average_gate_fidelity_target():
"""
Metrics: Tests that for random unitaries U, AGF(U, U) = 1.
"""
for _ in range(10):
U = rand_unitary_haar(13)
SU = to_super(U)
assert_almost_equal(average_gate_fidelity(SU, target=U), 1)
def test_average_gate_fidelity_target():
"""
Metrics: Tests that for random unitaries U, AGF(U, U) = 1.
"""
for _ in range(10):
U = rand_unitary_haar(13)
SU = to_super(U)
assert_almost_equal(average_gate_fidelity(SU, target=U), 1)
def test_dnorm_force_solve():
"""
Metrics: checks that special cases for dnorm agree with SDP solutions.
"""
def case(A, B, significant=4):
assert_approx_equal(
dnorm(A, B, force_solve=False), dnorm(A, B, force_solve=True)
)
for dim in (2, 3):
for _ in range(10):
yield (
case,
rand_super_bcsz(dim), None
)
for _ in range(10):
yield (
case,
rand_unitary_haar(dim), rand_unitary_haar(dim)
)
def random_herm_oper(_dims, n):
return make_hermitian(rand_unitary_haar(2**n, dims=_dims))