def lindbladian_epc_analytical(U_dict: dict, proj: bool):
real_cliffords = evaluate_sequences(U_dict, cliffords_decomp)
lvls = int(np.sqrt(real_cliffords[0].shape[0]))
projection = 'fulluni'
fid_lvls = lvls
if proj:
projection = 'wzeros'
fid_lvls = 2
ideal_cliffords = perfect_cliffords(lvls, projection)
fids = []
for C_indx in range(24):
C_real = real_cliffords[C_indx]
C_ideal = tf_super(
tf.constant(
ideal_cliffords[C_indx],
dtype=tf.complex128
)
)
ave_fid = tf_superoper_average_fidelity(C_real, C_ideal, lvls=fid_lvls)
fids.append(ave_fid)
infid = 1 - tf_ave(fids)
return infid
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)
def epc_analytical(U_dict: dict, index, dims, proj: bool):
# TODO make this work with new index and dims
gate = list(U_dict.keys())[0]
U = U_dict[gate]
num_gates = len(gate.split(":"))
lvls = int(U.shape[0] ** (1 / num_gates))
fid_lvls = lvls
if num_gates == 1:
real_cliffords = evaluate_sequences(U_dict, cliffords_decomp)
elif num_gates == 2:
real_cliffords = evaluate_sequences(U_dict, cliffords_decomp_xId)
projection = "fulluni"
if proj:
projection = "wzeros"
fid_lvls = 2 ** num_gates
ideal_cliffords = perfect_cliffords(str(lvls), proj=projection, num_gates=num_gates)
fids = []
for C_indx in range(24):
C_real = real_cliffords[C_indx]
C_ideal = ideal_cliffords[C_indx]
ave_fid = tf_average_fidelity(C_real, C_ideal, fid_lvls)
fids.append(ave_fid)
infid = 1 - tf_ave(fids)
return infid
def lindbladian_epc_analytical(U_dict: dict,
index,
dims,
proj: bool,
cliffords=False):
num_gates = len(dims)
if cliffords:
real_cliffords = evaluate_sequences(U_dict,
[[C] for C in cliffords_string])
elif num_gates == 1:
real_cliffords = evaluate_sequences(U_dict, cliffords_decomp)
elif num_gates == 2:
real_cliffords = evaluate_sequences(U_dict, cliffords_decomp_xId)
ideal_cliffords = perfect_cliffords(lvls=[2] * num_gates,
num_gates=num_gates)
fids = []
for C_indx in range(24):
C_real = real_cliffords[C_indx]
C_ideal = tf_super(
tf.constant(ideal_cliffords[C_indx], dtype=tf.complex128))
ave_fid = tf_superoper_average_fidelity(C_real, C_ideal, lvls=dims)
fids.append(ave_fid)
infid = 1 - tf_ave(fids)
return infid
def epc_analytical(propagators: dict,
index,
dims,
proj: bool,
cliffords=False):
# TODO check this work with new index and dims (double-check)
num_gates = len(dims)
if cliffords:
real_cliffords = evaluate_sequences(propagators,
[[C] for C in cliffords_string])
elif num_gates == 1:
real_cliffords = evaluate_sequences(propagators, cliffords_decomp)
elif num_gates == 2:
real_cliffords = evaluate_sequences(propagators, cliffords_decomp_xId)
ideal_cliffords = perfect_cliffords(lvls=[2] * num_gates,
num_gates=num_gates)
fids = []
for C_indx in range(24):
C_real = real_cliffords[C_indx]
C_ideal = tf.constant(ideal_cliffords[C_indx], dtype=tf.complex128)
ave_fid = tf_average_fidelity(C_real, C_ideal, lvls=dims)
fids.append(ave_fid)
infid = 1 - tf_ave(fids)
return infid