def get_average_fidelitiy(get_error_process):
lvls = [3, 3]
Lambda = get_error_process
d = 4
err = tf_super(Lambda)
choi = super_to_choi(err)
chi = tf_choi_to_chi(choi, dims=lvls)
fid = tf_abs((chi[0, 0] / d + 1) / (d + 1))
return fid
def orbit_infid(
U_dict,
RB_number: int = 30,
RB_length: int = 20,
lindbladian=False,
shots: int = None,
seqs=None,
noise=None
):
if not seqs:
seqs = single_length_RB(RB_number=RB_number, RB_length=RB_length)
Us = evaluate_sequences(U_dict, seqs)
infids = []
for U in Us:
dim = int(U.shape[0])
psi_init = tf.constant(basis(dim, 0), dtype=tf.complex128)
psi_actual = tf.matmul(U, psi_init)
pop0 = tf_abs(psi_actual[0])**2
p1 = 1 - pop0
if shots:
vals = tf.keras.backend.random_binomial(
[shots],
p=p1,
dtype=tf.float64,
)
# if noise:
# vals = vals + (np.random.randn(shots) * noise)
infid = tf.reduce_mean(vals)
else:
infid = p1
# if noise:
# infid = infid + (np.random.randn() * noise)
if noise:
infid = infid + (np.random.randn() * noise)
infids.append(infid)
return tf_ave(infids)