def adc_choi(x):
kraus = [
np.sqrt(1 - x) * qeye(2),
np.sqrt(x) * destroy(2),
np.sqrt(x) * fock_dm(2, 0),
]
return kraus_to_choi(kraus)
def choi(self):
if self._choi is not None:
return self._choi
elif self._kraus is not None:
self._choi = qt.kraus_to_choi(self._kraus)
elif self._channel_matrix is not None:
self._choi = qt.super_to_choi(self._channel_matrix)
return self._choi
def test_sparse_against_dense_adc(self, variable):
"""
Test sparse versus dense dnorm calculation for a sample of
amplitude-damping channels.
"""
# Choi matrix for identity channel on 1 qubit
A = kraus_to_choi([qeye(2)])
B = adc_choi(variable)
dense = dnorm(A, B, force_solve=True, sparse=False)
sparse = dnorm(A, B, force_solve=True, sparse=True)
assert dense == pytest.approx(sparse, abs=1e-7)
def __Cmatrix_old(self, kraus, S, dim):
rho = (1/dim)*qt.identity(dim)
klist = [rho*S.dag()*k.dag() for k in kraus]
return qt.kraus_to_choi(klist)