def test_ChoiKrausChoi(self):
"""
Superoperator: Converting superoperator to Choi matrix and back.
"""
h_5 = rand_herm(5)
superoperator = propagator(h_5, scipy.rand(),
[create(5), destroy(5), jmat(2, 'z')])
choi_matrix = super_to_choi(superoperator)
kraus_ops = choi_to_kraus(choi_matrix)
test_choi = kraus_to_choi(kraus_ops)
assert_((test_choi - choi_matrix).norm() < 1e-12)
def test_ChoiKrausChoi(self):
"""
Superoperator: Converting superoperator to Choi matrix and back.
"""
h_5 = rand_herm(5)
superoperator = propagator(
h_5, scipy.rand(),
[create(5), destroy(5), jmat(2, 'z')])
choi_matrix = super_to_choi(superoperator)
kraus_ops = choi_to_kraus(choi_matrix)
test_choi = kraus_to_choi(kraus_ops)
assert_((test_choi - choi_matrix).norm() < 1e-12)
def test_ChoiKrausChoi(self, superoperator):
"""
Superoperator: Convert superoperator to Choi matrix and back.
"""
choi_matrix = to_choi(superoperator)
kraus_ops = to_kraus(choi_matrix)
test_choi = kraus_to_choi(kraus_ops)
# Assert both that the result is close to expected, and has the right
# type.
assert (test_choi - choi_matrix).norm() < tol
assert choi_matrix.type == "super" and choi_matrix.superrep == "choi"
assert test_choi.type == "super" and test_choi.superrep == "choi"
def test_ChoiKrausChoi(self):
"""
Superoperator: Convert superoperator to Choi matrix and back.
"""
superoperator = rand_super()
choi_matrix = to_choi(superoperator)
kraus_ops = to_kraus(choi_matrix)
test_choi = kraus_to_choi(kraus_ops)
# Assert both that the result is close to expected, and has the right
# type.
assert_((test_choi - choi_matrix).norm() < tol)
assert_(choi_matrix.type == "super" and choi_matrix.superrep == "choi")
assert_(test_choi.type == "super" and test_choi.superrep == "choi")
def test_ChoiKrausChoi(self):
"""
Superoperator: Converting superoperator to Choi matrix and back.
"""
superoperator = rand_super()
choi_matrix = to_choi(superoperator)
kraus_ops = to_kraus(choi_matrix)
test_choi = kraus_to_choi(kraus_ops)
# Assert both that the result is close to expected, and has the right
# type.
assert_((test_choi - choi_matrix).norm() < 1e-12)
assert_(choi_matrix.type == "super" and choi_matrix.superrep == "choi")
assert_(test_choi.type == "super" and test_choi.superrep == "choi")
def test_dnorm_on_sparse_matrix(self):
"""
Tests sparse versus dense dnorm calculation on sparse matrices.
"""
force_solve = True
def AmpDampChoi(p):
Kraus = [(1 - p)**.5 * qeye(2), p**.5 * destroy(2),
p**.5 * fock_dm(2, 0)]
return kraus_to_choi(Kraus)
# Choi matrix for identity channel on 1 qubit
A = kraus_to_choi([qeye(2)])
p = np.random.uniform(0.1, 0.9)
B = AmpDampChoi(p)
dense_run_result = dnorm(A, B, force_solve=force_solve, sparse=False)
sparse_run_result = dnorm(A, B, force_solve=force_solve, sparse=True)
assert dense_run_result == pytest.approx(sparse_run_result, abs=1e-7)
def AmpDampChoi(p):
Kraus = [(1 - p)**.5 * qeye(2), p**.5 * destroy(2),
p**.5 * fock_dm(2, 0)]
return kraus_to_choi(Kraus)