def test_time_teleportation(self):
states = [z0, z1, xp, xm, yp, ym, rand_ket(2)]
for psi in states:
fidelity0000, fidelity = simulateTeleportation(
psi,
continuousTeleportationSimulation,
continuousXXBraidingCorrectionSimulation,
continuousZBraidingCorrectionSimulation,
continuousDecodingSimulation)
assert are_close(fidelity0000, 1.)
assert fidelity < fidelity0000
def test_unitary_teleportation(self):
states = [z0, z1, xp, xm, yp, ym, rand_ket(2)]
for psi in states:
fidelity0000, fidelity = simulateTeleportation(
psi,
circuitTeleportationSimulation,
circuitXXBraidingCorrectionSimulation,
circuitZBraidingCorrectionSimulation,
circuitDecodingSimulation)
assert are_close(fidelity0000, 1.)
assert are_close(fidelity, 1.)
def test_noisy_time_teleportation_rand(self):
N = 8
gamma = 0.05
c_ops = [np.sqrt(gamma)*Sz(N, i) for i in range(N)]
psi = rand_ket(2)
fidelity0000, fidelity = simulateTeleportation(
psi,
continuousTeleportationSimulation,
continuousXXBraidingCorrectionSimulation,
continuousZBraidingCorrectionSimulation,
continuousDecodingSimulation,
c_ops=c_ops)
assert fidelity0000 > fidelity
'xp': xp,
'xm': xm,
'yp': yp,
'ym': ym,
'rnd': rand_ket(2)
}
N = 8
nb = args.res
gmax = args.gamma
gs = np.linspace(0., gmax, nb)
psi = inp[args.psi]
results = np.zeros((3, nb))
results[0, :] = gs
for i, gamma in enumerate(tqdm(gs)):
c_ops = [np.sqrt(gamma) * Sz(N, j) for j in range(N)]
f0000, fall = simulateTeleportation(
psi,
continuousTeleportationSimulation,
continuousXXBraidingCorrectionSimulation,
continuousZBraidingCorrectionSimulation,
continuousDecodingSimulation,
c_ops=c_ops)
results[1, i] = f0000
results[2, i] = fall
np.save(args.output, np.array(results))