def term_func(Uk):
UkH = Uk.conj().T
res = 0.
for sig in sigs:
red = reduce(np.dot, [Uf, sig, UfH, Uk, sig, UkH])
res += np.trace(red)
return res
def fidSingleTxFull(rho_0, rho_f, T, N, Us):
U_k_ts = [np.array(U_k(T), np.complex128) for U_k in Us]
mats = [
np.dot(np.dot(rho_f.conj().T, U_k_t), np.dot(rho_0,
U_k_t.conj().T))
for U_k_t in U_k_ts
]
terms = [np.trace(mat) for mat in mats]
return (1. / N) * sum(terms)
def makeTerm(k):
# U_k = Us_mk[k]
# Us_nm_k = U_k[m:(n-1)]
# # Us_nm_k.reverse()
# Us_m1_k = U_k[0:(m-1)]
# # Us_m1_k.reverse()
#
# U_nm_k = reduce(np.dot, reversed(Us_nm_k),identity)
# U_nm_kH = U_nm_k.conj().T
# U_m1_k = reduce(np.dot, reversed(Us_m1_k),identity)
# U_m1_kH = U_m1_k.conj().T
#
# lambda_mk = reduce(np.dot, [U_nm_kH, rho_f, U_nm_k])
# lambda_H = lambda_mk.conj().T
# rho_mk = reduce(np.dot, [U_m1_k, rho_0, U_m1_kH])
U_k = Us_mk[k]
lambda_mk = makeLambda_mk(U_k, m, rho_f)
lambda_H = lambda_mk.conj().T
rho_mk = makeRho_mk(U_k, m, rho_0)
term = np.trace(np.dot(lambda_H, comm(sigmaX, rho_mk)))
return term
def fidSingleTxDirect(rho_f, rho, T):
return np.trace(np.dot(rho_f.conj().T, rho(T)))
def fidSingleTxDirect(rho_f, rho, T):
return np.trace(rho_f.H * rho(T))
def fidelity(self, rho_f):
return np.trace(rho_f.H * self.at(self.pulse_end_time))
