def TestPulseConstructor():
"""
Test for creating empty Pulse, Pulse with constant coefficients etc.
"""
coeff = np.array([0.1, 0.2, 0.3, 0.4])
tlist = np.array([0., 1., 2., 3.])
ham = sigmaz()
# Special ways of initializing pulse
pulse2 = Pulse(sigmax(), 0, tlist, True)
assert_allclose(pulse2.get_ideal_qobjevo(2).ops[0].qobj,
tensor(sigmax(), identity(2)))
pulse3 = Pulse(sigmay(), 0)
assert_allclose(pulse3.get_ideal_qobjevo(2).cte.norm(), 0.)
pulse4 = Pulse(None, None) # Dummy empty ham
assert_allclose(pulse4.get_ideal_qobjevo(2).cte.norm(), 0.)
tlist_noise = np.array([1., 2.5, 3.])
coeff_noise = np.array([0.5, 0.1, 0.5])
tlist_noise2 = np.array([0.5, 2, 3.])
coeff_noise2 = np.array([0.1, 0.2, 0.3])
# Pulse with different dims
random_qobj = Qobj(np.random.random((3, 3)))
pulse5 = Pulse(sigmaz(), 1, tlist, True)
pulse5.add_coherent_noise(sigmay(), 1, tlist_noise, coeff_noise)
pulse5.add_lindblad_noise(
random_qobj, 0, tlist=tlist_noise2, coeff=coeff_noise2)
qu, c_ops = pulse5.get_noisy_qobjevo(dims=[3, 2])
assert_allclose(qu.ops[0].qobj, tensor([identity(3), sigmaz()]))
assert_allclose(qu.ops[1].qobj, tensor([identity(3), sigmay()]))
assert_allclose(c_ops[0].ops[0].qobj, tensor([random_qobj, identity(2)]))
def TestBasicPulse():
"""
Test for basic pulse generation and attributes.
"""
coeff = np.array([0.1, 0.2, 0.3, 0.4])
tlist = np.array([0., 1., 2., 3.])
ham = sigmaz()
# Basic tests
pulse1 = Pulse(ham, 1, tlist, coeff)
assert_allclose(
pulse1.get_ideal_qobjevo(2).ops[0].qobj, tensor(identity(2), sigmaz()))
pulse1.tlist = 2 * tlist
assert_allclose(pulse1.tlist, 2 * tlist)
pulse1.tlist = tlist
pulse1.coeff = 2 * coeff
assert_allclose(pulse1.coeff, 2 * coeff)
pulse1.coeff = coeff
pulse1.qobj = 2 * sigmay()
assert_allclose(pulse1.qobj, 2 * sigmay())
pulse1.qobj = ham
pulse1.targets = 3
assert_allclose(pulse1.targets, 3)
pulse1.targets = 1
assert_allclose(pulse1.get_ideal_qobj(2), tensor(identity(2), sigmaz()))
def TestCoherentNoise():
"""
Test for pulse genration with coherent noise.
"""
coeff = np.array([0.1, 0.2, 0.3, 0.4])
tlist = np.array([0., 1., 2., 3.])
ham = sigmaz()
pulse1 = Pulse(ham, 1, tlist, coeff)
# Add coherent noise with the same tlist
pulse1.add_coherent_noise(sigmay(), 0, tlist, coeff)
assert_allclose(
pulse1.get_ideal_qobjevo(2).ops[0].qobj, tensor(identity(2), sigmaz()))
assert_(len(pulse1.coherent_noise) == 1)
noise_qu, c_ops = pulse1.get_noisy_qobjevo(2)
assert_allclose(c_ops, [])
assert_allclose(noise_qu.tlist, np.array([0., 1., 2., 3.]))
qobj_list = [ele.qobj for ele in noise_qu.ops]
assert_(tensor(identity(2), sigmaz()) in qobj_list)
assert_(tensor(sigmay(), identity(2)) in qobj_list)
for ele in noise_qu.ops:
assert_allclose(ele.coeff, coeff)
def TestNoisyPulse():
"""
Test for lindblad noise and different tlist
"""
coeff = np.array([0.1, 0.2, 0.3, 0.4])
tlist = np.array([0., 1., 2., 3.])
ham = sigmaz()
pulse1 = Pulse(ham, 1, tlist, coeff)
# Add coherent noise and lindblad noise with different tlist
pulse1.spline_kind = "step_func"
tlist_noise = np.array([1., 2.5, 3.])
coeff_noise = np.array([0.5, 0.1, 0.5])
pulse1.add_coherent_noise(sigmay(), 0, tlist_noise, coeff_noise)
tlist_noise2 = np.array([0.5, 2, 3.])
coeff_noise2 = np.array([0.1, 0.2, 0.3])
pulse1.add_lindblad_noise(sigmax(), 1, coeff=True)
pulse1.add_lindblad_noise(
sigmax(), 0, tlist=tlist_noise2, coeff=coeff_noise2)
assert_allclose(
pulse1.get_ideal_qobjevo(2).ops[0].qobj, tensor(identity(2), sigmaz()))
noise_qu, c_ops = pulse1.get_noisy_qobjevo(2)
assert_allclose(noise_qu.tlist, np.array([0., 0.5, 1., 2., 2.5, 3.]))
for ele in noise_qu.ops:
if ele.qobj == tensor(identity(2), sigmaz()):
assert_allclose(
ele.coeff, np.array([0.1, 0.1, 0.2, 0.3, 0.3, 0.4]))
elif ele.qobj == tensor(sigmay(), identity(2)):
assert_allclose(
ele.coeff, np.array([0., 0., 0.5, 0.5, 0.1, 0.5]))
for c_op in c_ops:
if len(c_op.ops) == 0:
assert_allclose(c_ops[0].cte, tensor(identity(2), sigmax()))
else:
assert_allclose(
c_ops[1].ops[0].qobj, tensor(sigmax(), identity(2)))
assert_allclose(
c_ops[1].tlist, np.array([0., 0.5, 1., 2., 2.5, 3.]))
assert_allclose(
c_ops[1].ops[0].coeff, np.array([0., 0.1, 0.1, 0.2, 0.2, 0.3]))