def test_stinespring_dims(self, dimension):
"""
Stinespring: Check that dims of channels are preserved.
"""
chan = super_tensor(to_super(sigmax()), to_super(qeye(dimension)))
A, B = to_stinespring(chan)
assert A.dims == [[2, dimension, 1], [2, dimension]]
assert B.dims == [[2, dimension, 1], [2, dimension]]
def test_stinespring_cp(self, dimension):
"""
Stinespring: A and B match for CP maps.
"""
superop = rand_super_bcsz(dimension)
A, B = to_stinespring(superop)
assert norm(A - B) < tol
def test_stinespring_dims(self):
"""
Stinespring: Check that dims of channels are preserved.
"""
# FIXME: not the most general test, since this assumes a map
# from square matrices to square matrices on the same space.
chan = super_tensor(to_super(sigmax()), to_super(qeye(3)))
A, B = to_stinespring(chan)
assert_equal(A.dims, [[2, 3, 1], [2, 3]])
assert_equal(B.dims, [[2, 3, 1], [2, 3]])
def case(map, state):
S = to_super(map)
A, B = to_stinespring(map)
q1 = vector_to_operator(S * operator_to_vector(state))
# FIXME: problem if Kraus index is implicitly
# ptraced!
q2 = (A * state * B.dag()).ptrace((0, ))
assert_((q1 - q2).norm('tr') <= thresh)
def test_stinespring_dims(self):
"""
Stinespring: Check that dims of channels are preserved.
"""
# FIXME: not the most general test, since this assumes a map
# from square matrices to square matrices on the same space.
chan = super_tensor(to_super(sigmax()), to_super(qeye(3)))
A, B = to_stinespring(chan)
assert_equal(A.dims, [[2, 3, 1], [2, 3]])
assert_equal(B.dims, [[2, 3, 1], [2, 3]])
def case(map, state):
S = to_super(map)
A, B = to_stinespring(map)
q1 = vector_to_operator(
S * operator_to_vector(state)
)
# FIXME: problem if Kraus index is implicitly
# ptraced!
q2 = (A * state * B.dag()).ptrace((0,))
assert_((q1 - q2).norm('tr') <= thresh)
def test_stinespring_agrees(self, dimension):
"""
Stinespring: Partial Tr over pair agrees w/ supermatrix.
"""
map = rand_super_bcsz(dimension)
state = rand_dm_ginibre(dimension)
S = to_super(map)
A, B = to_stinespring(map)
q1 = vector_to_operator(S * operator_to_vector(state))
# FIXME: problem if Kraus index is implicitly
# ptraced!
q2 = (A * state * B.dag()).ptrace((0, ))
assert (q1 - q2).norm('tr') <= tol
def case(map):
A, B = to_stinespring(map)
assert_(norm((A - B).data.todense()) < thresh)
def case(map):
A, B = to_stinespring(map)
assert_(norm((A - B).data.todense()) < thresh)
def case(map):
A, B = to_stinespring(map)
assert_(norm((A - B).full()) < thresh)
