def test_no_saving_intermidiate_state(self):
processor = Processor(1)
processor.add_pulse(pulse=Pulse(sigmax(),
coeff=np.ones(10),
tlist=np.linspace(0, 1, 10),
targets=0))
result = processor.run_state(basis(2, 0), tlist=[0, 1])
assert (len(result.states) == 2)
def test_id_with_T1_T2(self):
"""
Test for identity evolution with relaxation t1 and t2
"""
# setup
a = destroy(2)
Hadamard = hadamard_transform(1)
ex_state = basis(2, 1)
mines_state = (basis(2, 1) - basis(2, 0)).unit()
end_time = 2.
tlist = np.arange(0, end_time + 0.02, 0.02)
t1 = 1.
t2 = 0.5
# test t1
test = Processor(1, t1=t1)
# zero ham evolution
test.add_pulse(Pulse(identity(2), 0, tlist, False))
result = test.run_state(ex_state, e_ops=[a.dag() * a])
assert_allclose(result.expect[0][-1],
np.exp(-1. / t1 * end_time),
rtol=1e-5,
err_msg="Error in t1 time simulation")
# test t2
test = Processor(1, t2=t2)
test.add_pulse(Pulse(identity(2), 0, tlist, False))
result = test.run_state(init_state=mines_state,
e_ops=[Hadamard * a.dag() * a * Hadamard])
assert_allclose(result.expect[0][-1],
np.exp(-1. / t2 * end_time) * 0.5 + 0.5,
rtol=1e-5,
err_msg="Error in t2 time simulation")
# test t1 and t2
t1 = np.random.rand(1) + 0.5
t2 = np.random.rand(1) * 0.5 + 0.5
test = Processor(1, t1=t1, t2=t2)
test.add_pulse(Pulse(identity(2), 0, tlist, False))
result = test.run_state(init_state=mines_state,
e_ops=[Hadamard * a.dag() * a * Hadamard])
assert_allclose(result.expect[0][-1],
np.exp(-1. / t2 * end_time) * 0.5 + 0.5,
rtol=1e-5,
err_msg="Error in t1 & t2 simulation, "
"with t1={} and t2={}".format(t1, t2))
def testDrift(self):
"""
Test for the drift Hamiltonian
"""
processor = Processor(N=1)
processor.add_drift(sigmaz(), 0)
tlist = np.array([0., 1., 2.])
processor.add_pulse(Pulse(identity(2), 0, tlist, False))
ideal_qobjevo, _ = processor.get_qobjevo(noisy=True)
assert_equal(ideal_qobjevo.cte, sigmaz())
def test_user_defined_noise(self):
"""
Test for the user-defined noise object
"""
dr_noise = DriftNoise(sigmax())
proc = Processor(1)
proc.add_noise(dr_noise)
tlist = np.array([0, np.pi/2.])
proc.add_pulse(Pulse(identity(2), 0, tlist, False))
result = proc.run_state(init_state=basis(2, 0))
assert_allclose(
fidelity(result.states[-1], basis(2, 1)), 1, rtol=1.0e-6)
def test_id_evolution(self):
"""
Test for identity evolution
"""
N = 1
proc = Processor(N=N)
init_state = rand_ket(2)
tlist = [0., 1., 2.]
proc.add_pulse(Pulse(identity(2), 0, tlist, False))
result = proc.run_state(
init_state, options=Options(store_final_state=True))
global_phase = init_state.data[0, 0]/result.final_state.data[0, 0]
assert_allclose(global_phase*result.final_state, init_state)
def TestControlAmpNoise(self):
"""
Test for the control amplitude noise
"""
tlist = np.array([1, 2, 3, 4, 5, 6])
coeff = np.array([1, 1, 1, 1, 1, 1])
# use proc_qobjevo
pulses = [Pulse(sigmaz(), 0, tlist, coeff)]
connoise = ControlAmpNoise(coeff=coeff, tlist=tlist)
noisy_pulses = connoise.get_noisy_dynamics(pulses=pulses)
assert_allclose(noisy_pulses[0].coherent_noise[0].qobj, sigmaz())
assert_allclose(noisy_pulses[0].coherent_noise[0].coeff, coeff)
def test_relaxation_noise(self):
"""
Test for the relaxation noise
"""
# only t1
a = destroy(2)
dims = [2] * 3
relnoise = RelaxationNoise(t1=[1., 1., 1.], t2=None)
systematic_noise = Pulse(None, None, label="system")
pulses, systematic_noise = relnoise.get_noisy_pulses(
dims=dims, systematic_noise=systematic_noise)
noisy_qu, c_ops = systematic_noise.get_noisy_qobjevo(dims=dims)
assert_(len(c_ops) == 3)
assert_allclose(c_ops[1](0).full(),
tensor([qeye(2), a, qeye(2)]).full())
# no relaxation
dims = [2] * 2
relnoise = RelaxationNoise(t1=None, t2=None)
systematic_noise = Pulse(None, None, label="system")
pulses, systematic_noise = relnoise.get_noisy_pulses(
dims=dims, systematic_noise=systematic_noise)
noisy_qu, c_ops = systematic_noise.get_noisy_qobjevo(dims=dims)
assert_(len(c_ops) == 0)
# only t2
relnoise = RelaxationNoise(t1=None, t2=[0.2, 0.7])
systematic_noise = Pulse(None, None, label="system")
pulses, systematic_noise = relnoise.get_noisy_pulses(
dims=dims, systematic_noise=systematic_noise)
noisy_qu, c_ops = systematic_noise.get_noisy_qobjevo(dims=dims)
assert_(len(c_ops) == 2)
# t1+t2 and systematic_noise = None
relnoise = RelaxationNoise(t1=[1., 1.], t2=[0.5, 0.5])
pulses, systematic_noise = relnoise.get_noisy_pulses(dims=dims)
noisy_qu, c_ops = systematic_noise.get_noisy_qobjevo(dims=dims)
assert_(len(c_ops) == 4)
def test_control_amplitude_noise(self):
"""
Test for the control amplitude noise
"""
tlist = np.array([1, 2, 3, 4, 5, 6])
coeff = np.array([1, 1, 1, 1, 1, 1])
# use proc_qobjevo
pulses = [Pulse(sigmaz(), 0, tlist, coeff)]
connoise = ControlAmpNoise(coeff=coeff, tlist=tlist)
noisy_pulses, systematic_noise = \
connoise.get_noisy_pulses(pulses=pulses)
assert_allclose(pulses[0].coherent_noise[0].qobj.full(),
sigmaz().full())
assert_allclose(noisy_pulses[0].coherent_noise[0].coeff, coeff)
def testDrift(self):
"""
Test for the drift Hamiltonian
"""
processor = Processor(N=1)
processor.add_drift(sigmax() / 2, 0)
tlist = np.array([0., np.pi, 2 * np.pi, 3 * np.pi])
processor.add_pulse(Pulse(None, None, tlist, False))
ideal_qobjevo, _ = processor.get_qobjevo(noisy=True)
assert_equal(ideal_qobjevo(0), sigmax() / 2)
init_state = basis(2)
propagators = processor.run_analytically()
analytical_result = init_state
for unitary in propagators:
analytical_result = unitary * analytical_result
fid = fidelity(sigmax() * init_state, analytical_result)
assert ((1 - fid) < 1.0e-6)
def test_coherent_noise(self):
"""
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)(0).full(),
tensor(identity(2), sigmaz()).full() * 0.1)
assert_(len(pulse1.coherent_noise) == 1)
noise_qu, c_ops = pulse1.get_noisy_qobjevo(2)
assert_allclose(c_ops, [])
assert_allclose(pulse1.get_full_tlist(), np.array([0., 1., 2., 3.]))
def test_decoherence_noise(self):
"""
Test for the decoherence noise
"""
tlist = np.array([0, 1, 2, 3, 4, 5, 6])
coeff = np.array([1, 1, 1, 1, 1, 1, 1])
# Time-dependent
decnoise = DecoherenceNoise(sigmaz(),
coeff=coeff,
tlist=tlist,
targets=[1])
dims = [2] * 2
systematic_noise = Pulse(None,
None,
label="systematic_noise",
spline_kind="step_func")
pulses, systematic_noise = decnoise.get_noisy_pulses(
systematic_noise=systematic_noise, dims=dims)
noisy_qu, c_ops = systematic_noise.get_noisy_qobjevo(dims=dims)
assert_allclose(c_ops[0](0).full(), tensor(qeye(2), sigmaz()).full())
# Time-independent and all qubits
decnoise = DecoherenceNoise(sigmax(), all_qubits=True)
pulses, systematic_noise = decnoise.get_noisy_pulses(dims=dims)
noisy_qu, c_ops = systematic_noise.get_noisy_qobjevo(dims=dims)
c_ops = [qu(0) for qu in c_ops]
assert_(tensor([qeye(2), sigmax()]) in c_ops)
assert_(tensor([sigmax(), qeye(2)]) in c_ops)
# Time-denpendent and all qubits
decnoise = DecoherenceNoise(sigmax(),
all_qubits=True,
coeff=coeff * 2,
tlist=tlist)
systematic_noise = Pulse(None,
None,
label="systematic_noise",
spline_kind="step_func")
pulses, systematic_noise = decnoise.get_noisy_pulses(
systematic_noise=systematic_noise, dims=dims)
noisy_qu, c_ops = systematic_noise.get_noisy_qobjevo(dims=dims)
assert_allclose(c_ops[0](0).full(),
tensor(sigmax(), qeye(2)).full() * 2)
assert_allclose(c_ops[1](0).full(),
tensor(qeye(2), sigmax()).full() * 2)
def test_user_defined_noise(noise_class, mode):
"""
Test for the user-defined noise object
"""
dr_noise = noise_class(sigmax())
proc = Processor(1)
proc.add_noise(dr_noise)
tlist = np.array([0, np.pi / 2.])
proc.add_pulse(Pulse(identity(2), 0, tlist, False))
if mode == "warning":
with pytest.warns(PendingDeprecationWarning):
result = proc.run_state(init_state=basis(2, 0))
elif mode == "error":
with pytest.raises(NotImplementedError):
result = proc.run_state(init_state=basis(2, 0))
return
else:
result = proc.run_state(init_state=basis(2, 0))
assert_allclose(fidelity(result.states[-1], basis(2, 1)), 1, rtol=1.0e-6)
def test_random_noise(self):
"""
Test for the white noise
"""
tlist = np.array([1, 2, 3, 4, 5, 6])
coeff = np.array([1, 1, 1, 1, 1, 1])
dummy_qobjevo = QobjEvo(sigmaz(), tlist=tlist)
mean = 0.
std = 0.5
pulses = [
Pulse(sigmaz(), 0, tlist, coeff),
Pulse(sigmax(), 0, tlist, coeff * 2),
Pulse(sigmay(), 0, tlist, coeff * 3)
]
# random noise with operators from proc_qobjevo
gaussnoise = RandomNoise(dt=0.1,
rand_gen=np.random.normal,
loc=mean,
scale=std)
noisy_pulses, systematic_noise = \
gaussnoise.get_noisy_pulses(pulses=pulses)
assert_allclose(noisy_pulses[2].qobj.full(), sigmay().full())
assert_allclose(noisy_pulses[1].coherent_noise[0].qobj.full(),
sigmax().full())
assert_allclose(len(noisy_pulses[0].coherent_noise[0].tlist),
len(noisy_pulses[0].coherent_noise[0].coeff))
# random noise with dt and other random number generator
pulses = [
Pulse(sigmaz(), 0, tlist, coeff),
Pulse(sigmax(), 0, tlist, coeff * 2),
Pulse(sigmay(), 0, tlist, coeff * 3)
]
gaussnoise = RandomNoise(lam=0.1, dt=0.2, rand_gen=np.random.poisson)
assert_(gaussnoise.rand_gen is np.random.poisson)
noisy_pulses, systematic_noise = \
gaussnoise.get_noisy_pulses(pulses=pulses)
assert_allclose(noisy_pulses[0].coherent_noise[0].tlist,
np.linspace(1, 6,
int(5 / 0.2) + 1))
assert_allclose(noisy_pulses[1].coherent_noise[0].tlist,
np.linspace(1, 6,
int(5 / 0.2) + 1))
assert_allclose(noisy_pulses[2].coherent_noise[0].tlist,
np.linspace(1, 6,
int(5 / 0.2) + 1))
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]))
def test_repeated_use_of_processor(self):
processor = Processor(1, t1=1.)
processor.add_pulse(Pulse(sigmax(), targets=0, coeff=True))
result1 = processor.run_state(basis(2, 0), tlist=np.linspace(0, 1, 10))
result2 = processor.run_state(basis(2, 0), tlist=np.linspace(0, 1, 10))
assert_allclose(result1.states[-1].full(), result2.states[-1].full())
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 test_noisy_pulse(self):
"""
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([0., 1., 2.5, 3.])
coeff_noise = np.array([0., 0.5, 0.1, 0.5])
pulse1.add_coherent_noise(sigmay(), 0, tlist_noise, coeff_noise)
tlist_noise2 = np.array([0., 0.5, 2, 3.])
coeff_noise2 = np.array([0., 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)(0).full(),
tensor(identity(2), sigmaz()).full() * 0.1)
noise_qu, c_ops = pulse1.get_noisy_qobjevo(2)
assert_allclose(pulse1.get_full_tlist(),
np.array([0., 0.5, 1., 2., 2.5, 3.]))
if parse_version(qutip.__version__) >= parse_version('5.dev'):
expected = QobjEvo([[
tensor(identity(2), sigmaz()),
np.array([0.1, 0.1, 0.2, 0.3, 0.3, 0.4])
],
[
tensor(sigmay(), identity(2)),
np.array([0., 0., 0.5, 0.5, 0.1, 0.5])
]],
tlist=np.array([0., 0.5, 1., 2., 2.5, 3.]),
order=0)
else:
expected = QobjEvo([[
tensor(identity(2), sigmaz()),
np.array([0.1, 0.1, 0.2, 0.3, 0.3, 0.4])
],
[
tensor(sigmay(), identity(2)),
np.array([0., 0., 0.5, 0.5, 0.1, 0.5])
]],
tlist=np.array([0., 0.5, 1., 2., 2.5, 3.]),
args={"_step_func_coeff": True})
_compare_qobjevo(noise_qu, expected, 0, 3)
for c_op in c_ops:
try:
isconstant = c_op.isconstant
except AttributeError:
isconstant = (len(c_op.ops) == 0)
if isconstant:
assert_allclose(
c_op(0).full(),
tensor(identity(2), sigmax()).full())
else:
if parse_version(qutip.__version__) >= parse_version('5.dev'):
expected = QobjEvo([
tensor(sigmax(), identity(2)),
np.array([0., 0.1, 0.1, 0.2, 0.2, 0.3])
],
tlist=np.array(
[0., 0.5, 1., 2., 2.5, 3.]),
order=0)
else:
expected = QobjEvo([
tensor(sigmax(), identity(2)),
np.array([0., 0.1, 0.1, 0.2, 0.2, 0.3])
],
tlist=np.array(
[0., 0.5, 1., 2., 2.5, 3.]),
args={"_step_func_coeff": True})
_compare_qobjevo(c_op, expected, 0, 3)
def test_basic_pulse(self):
"""
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)(0).full(),
tensor(identity(2), sigmaz()).full() * coeff[0])
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.full(), 2 * sigmay().full())
pulse1.qobj = ham
pulse1.targets = 3
assert_allclose(pulse1.targets, 3)
pulse1.targets = 1
qobjevo = pulse1.get_ideal_qobjevo(2)
if parse_version(qutip.__version__) >= parse_version('5.dev'):
expected = QobjEvo([tensor(identity(2), sigmaz()), coeff],
tlist=tlist,
order=0)
else:
expected = QobjEvo([tensor(identity(2), sigmaz()), coeff],
tlist=tlist,
args={"_step_func_coeff": True})
_compare_qobjevo(qobjevo, expected, 0, 3)
def test_pulse_constructor(self):
"""
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)(0).full(),
tensor(sigmax(), identity(2)).full())
pulse3 = Pulse(sigmay(), 0)
assert_allclose(pulse3.get_ideal_qobjevo(2)(0).norm(), 0.)
pulse4 = Pulse(None, None) # Dummy empty ham
assert_allclose(pulse4.get_ideal_qobjevo(2)(0).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])
if parse_version(qutip.__version__) >= parse_version('5.dev'):
expected = QobjEvo([
tensor([identity(3), sigmaz()]),
[tensor([identity(3), sigmay()]), coeff_noise]
],
tlist=tlist_noise,
order=0)
else:
expected = QobjEvo([
tensor([identity(3), sigmaz()]),
[tensor([identity(3), sigmay()]), coeff_noise]
],
tlist=tlist_noise,
args={"_step_func_coeff": True})
_compare_qobjevo(qu, expected, 0, 3)
def get_noisy_dynamics(self, ctrl_pulses, dims=None):
dummy = Pulse(None, None)
dummy.add_coherent_noise(self.qobj, 0, coeff=True)
return ctrl_pulses + [dummy]
