def test_c_ops_fn_list_td_corr():
"""
correlation: comparing 3LS emission corr., c_ops td (fn-list td format)
"""
# calculate zero-delay HOM cross-correlation, for incoherently pumped
# 3LS ladder system g2ab[0]
# with following parameters:
# gamma = 1, 99% initialized, tp = 0.5
# Then: g2ab(0)~0.185
tlist = np.linspace(0, 6, 20)
ket0 = fock(3, 0)
ket1 = fock(3, 1)
ket2 = fock(3, 2)
sm01 = ket0 * ket1.dag()
sm12 = ket1 * ket2.dag()
psi0 = fock(3, 2)
tp = 1
# define "pi" pulse as when 99% of population has been transferred
Om = np.sqrt(-np.log(1e-2) / (tp * np.sqrt(np.pi)))
c_ops = [
sm01,
[
sm12 * Om, lambda t, args: np.exp(-(t - args["t_off"])**2 /
(2 * args["tp"]**2))
]
]
args = {"tp": tp, "t_off": 2}
H = qeye(3) * 0
# HOM cross-correlation depends on coherences (g2[0]=0)
c1 = correlation_2op_2t(H,
psi0,
tlist,
tlist,
c_ops,
sm01.dag(),
sm01,
args=args)
c2 = correlation_2op_2t(H,
psi0,
tlist,
tlist,
c_ops,
sm01.dag(),
sm01,
args=args,
reverse=True)
n = mesolve(H, psi0, tlist, c_ops, [sm01.dag() * sm01],
args=args).expect[0]
n_f = Cubic_Spline(tlist[0], tlist[-1], n)
corr_ab = -c1 * c2 + np.array([[n_f(t) * n_f(t + tau) for tau in tlist]
for t in tlist])
dt = tlist[1] - tlist[0]
gab = abs(np.trapz(np.trapz(corr_ab, axis=0))) * dt**2
assert_(abs(gab - 0.185) < 1e-2)
def make_ham_spline(self, times, pulse):
'''
:param times: time axis of pulse (Omega)
:param pulse: y data for applied pulse
:type times: numpy array
:type pulse: numpy array
'''
#Hamiltonian
H0 = self.Delta1 * two_zero * two_zero.dag() + (self.Delta1 - self.deltaAC) * zero_one * zero_one.dag() \
+ self.g * zero_one * two_zero.dag() + np.conj(self.g) * two_zero * zero_one.dag()
H1 = two_zero * one_zero.dag()
H2 = one_zero * two_zero.dag()
S = Cubic_Spline(times[0], times[-1], pulse)
S_conj = Cubic_Spline(times[0], times[-1], np.conj(pulse))
H = [H0, [H1, S], [H2, S_conj]]
return H