def test_01_6_unitary_hadamard_grad(self):
"""
control.pulseoptim: Hadamard gate gradient check
assert that gradient approx and exact gradient match in tolerance
"""
# Hadamard
H_d = sigmaz()
H_c = [sigmax()]
U_0 = identity(2)
U_targ = hadamard_transform(1)
n_ts = 10
evo_time = 10
# Create the optim objects
optim = cpo.create_pulse_optimizer(H_d, H_c, U_0, U_targ,
n_ts, evo_time,
fid_err_targ=1e-10,
dyn_type='UNIT',
init_pulse_type='LIN',
gen_stats=True)
dyn = optim.dynamics
init_amps = optim.pulse_generator.gen_pulse().reshape([-1, 1])
dyn.initialize_controls(init_amps)
# Check the exact gradient
func = optim.fid_err_func_wrapper
grad = optim.fid_err_grad_wrapper
x0 = dyn.ctrl_amps.flatten()
grad_diff = check_grad(func, grad, x0)
assert_almost_equal(grad_diff, 0.0, decimal=6,
err_msg="Unitary gradient outside tolerance")
def count_waves(n_ts, evo_time, ptype, freq=None, num_waves=None):
# Any dyn config will do
#Hadamard
H_d = sigmaz()
H_c = [sigmax()]
U_0 = identity(2)
U_targ = hadamard_transform(1)
pulse_params = {}
if freq is not None:
pulse_params['freq'] = freq
if num_waves is not None:
pulse_params['num_waves'] = num_waves
optim = cpo.create_pulse_optimizer(H_d, H_c, U_0, U_targ,
n_ts, evo_time,
dyn_type='UNIT',
init_pulse_type=ptype,
init_pulse_params=pulse_params,
gen_stats=False)
pgen = optim.pulse_generator
pulse = pgen.gen_pulse()
# count number of waves
zero_cross = pulse[0:-2]*pulse[1:-1] < 0
return (sum(zero_cross) + 1) / 2
def test_11_time_dependent_drift(self):
"""
control.pulseoptim: Hadamard gate with fixed and time varying drift
assert that goal is achieved for both and that different control
pulses are produced (only) when they should be
"""
# Hadamard
H_0 = sigmaz()
H_c = [sigmax()]
U_0 = identity(2)
U_targ = hadamard_transform(1)
n_ts = 20
evo_time = 10
drift_amps_flat = np.ones([n_ts], dtype=float)
dript_amps_step = [np.round(float(k)/n_ts) for k in range(n_ts)]
# Run the optimisations
result_fixed = cpo.optimize_pulse_unitary(H_0, H_c, U_0, U_targ,
n_ts, evo_time,
fid_err_targ=1e-10,
init_pulse_type='LIN',
gen_stats=True)
assert_(result_fixed.goal_achieved,
msg="Fixed drift goal not achieved. "
"Terminated due to: {}, with infidelity: {}".format(
result_fixed.termination_reason, result_fixed.fid_err))
H_d = [drift_amps_flat[k]*H_0 for k in range(n_ts)]
result_flat = cpo.optimize_pulse_unitary(H_d, H_c, U_0, U_targ,
n_ts, evo_time,
fid_err_targ=1e-10,
init_pulse_type='LIN',
gen_stats=True)
assert_(result_flat.goal_achieved, msg="Flat drift goal not achieved. "
"Terminated due to: {}, with infidelity: {}".format(
result_flat.termination_reason, result_flat.fid_err))
# Check fixed and flat produced the same pulse
assert_almost_equal(result_fixed.final_amps, result_flat.final_amps,
decimal=9,
err_msg="Flat and fixed drift result in "
"different control pules")
H_d = [dript_amps_step[k]*H_0 for k in range(n_ts)]
result_step = cpo.optimize_pulse_unitary(H_d, H_c, U_0, U_targ,
n_ts, evo_time,
fid_err_targ=1e-10,
init_pulse_type='LIN',
gen_stats=True)
assert_(result_step.goal_achieved, msg="Step drift goal not achieved. "
"Terminated due to: {}, with infidelity: {}".format(
result_step.termination_reason, result_step.fid_err))
# Check step and flat produced different results
assert_(np.any(
np.abs(result_flat.final_amps - result_step.final_amps) > 1e-3),
msg="Flat and step drift result in "
"the same control pules")
def test_01_4_unitary_hadamard_qobj(self):
"""
control.pulseoptim: Hadamard gate with linear initial pulses (Qobj)
assert that goal is achieved
"""
# Hadamard
H_d = sigmaz()
H_c = [sigmax()]
U_0 = identity(2)
U_targ = hadamard_transform(1)
n_ts = 10
evo_time = 10
# Run the optimisation
#Try with Qobj propagation
result = cpo.optimize_pulse_unitary(H_d, H_c, U_0, U_targ,
n_ts, evo_time,
fid_err_targ=1e-10,
init_pulse_type='LIN',
dyn_params={'oper_dtype':Qobj},
gen_stats=True)
assert_(result.goal_achieved, msg="Hadamard goal not achieved "
"(Qobj propagation). "
"Terminated due to: {}, with infidelity: {}".format(
result.termination_reason, result.fid_err))
def test_01_1_unitary_hadamard(self):
"""
control.pulseoptim: Hadamard gate with linear initial pulses
assert that goal is achieved and fidelity error is below threshold
"""
# Hadamard
H_d = sigmaz()
H_c = [sigmax()]
U_0 = identity(2)
U_targ = hadamard_transform(1)
n_ts = 10
evo_time = 10
# Run the optimisation
result = cpo.optimize_pulse_unitary(H_d, H_c, U_0, U_targ,
n_ts, evo_time,
fid_err_targ=1e-10,
init_pulse_type='LIN',
gen_stats=True)
assert_(result.goal_achieved, msg="Hadamard goal not achieved. "
"Terminated due to: {}, with infidelity: {}".format(
result.termination_reason, result.fid_err))
assert_almost_equal(result.fid_err, 0.0, decimal=10,
err_msg="Hadamard infidelity too high")
def test_01_2_unitary_hadamard_no_stats(self):
"""
control.pulseoptim: Hadamard gate with linear initial pulses (no stats)
assert that goal is achieved
"""
# Hadamard
H_d = sigmaz()
H_c = [sigmax()]
U_0 = identity(2)
U_targ = hadamard_transform(1)
n_ts = 10
evo_time = 10
# Run the optimisation
#Try without stats
result = cpo.optimize_pulse_unitary(H_d, H_c, U_0, U_targ,
n_ts, evo_time,
fid_err_targ=1e-10,
init_pulse_type='LIN',
gen_stats=False)
assert_(result.goal_achieved, msg="Hadamard goal not achieved "
"(no stats). "
"Terminated due to: {}, with infidelity: {}".format(
result.termination_reason, result.fid_err))
def test_08_crab(self):
"""
control.pulseoptim: Hadamard gate using CRAB algorithm
Apply guess and ramping pulse
assert that goal is achieved and fidelity error is below threshold
assert that starting amplitude is zero
"""
# Hadamard
H_d = sigmaz()
H_c = [sigmax()]
U_0 = identity(2)
U_targ = hadamard_transform(1)
n_ts = 12
evo_time = 10
# Run the optimisation
result = cpo.opt_pulse_crab_unitary(H_d, H_c, U_0, U_targ,
n_ts, evo_time,
fid_err_targ=1e-5,
alg_params={'crab_pulse_params':{'randomize_coeffs':False,
'randomize_freqs':False}},
init_coeff_scaling=0.5,
guess_pulse_type='GAUSSIAN',
guess_pulse_params={'variance':0.1*evo_time},
guess_pulse_scaling=1.0, guess_pulse_offset=1.0,
amp_lbound=None, amp_ubound=None,
ramping_pulse_type='GAUSSIAN_EDGE',
ramping_pulse_params={'decay_time':evo_time/100.0},
gen_stats=True)
assert_(result.goal_achieved, msg="Hadamard goal not achieved. "
"Terminated due to: {}, with infidelity: {}".format(
result.termination_reason, result.fid_err))
assert_almost_equal(result.fid_err, 0.0, decimal=3,
err_msg="Hadamard infidelity too high")
assert_almost_equal(result.final_amps[0, 0], 0.0, decimal=3,
err_msg="lead in amplitude not zero")
# Repeat with Qobj integration
result = cpo.opt_pulse_crab_unitary(H_d, H_c, U_0, U_targ,
n_ts, evo_time,
fid_err_targ=1e-5,
alg_params={'crab_pulse_params':{'randomize_coeffs':False,
'randomize_freqs':False}},
dyn_params={'oper_dtype':Qobj},
init_coeff_scaling=0.5,
guess_pulse_type='GAUSSIAN',
guess_pulse_params={'variance':0.1*evo_time},
guess_pulse_scaling=1.0, guess_pulse_offset=1.0,
amp_lbound=None, amp_ubound=None,
ramping_pulse_type='GAUSSIAN_EDGE',
ramping_pulse_params={'decay_time':evo_time/100.0},
gen_stats=True)
assert_(result.goal_achieved, msg="Hadamard goal not achieved"
"(Qobj integration). "
"Terminated due to: {}, with infidelity: {}".format(
result.termination_reason, result.fid_err))
def test_hadamard_explicit(self):
test = gates.hadamard_transform(3).full()
expected = np.array([[1, 1, 1, 1, 1, 1, 1, 1],
[1, -1, 1, -1, 1, -1, 1, -1],
[1, 1, -1, -1, 1, 1, -1, -1],
[1, -1, -1, 1, 1, -1, -1, 1],
[1, 1, 1, 1, -1, -1, -1, -1],
[1, -1, 1, -1, -1, 1, -1, 1],
[1, 1, -1, -1, -1, -1, 1, 1],
[1, -1, -1, 1, -1, 1, 1, -1]])
expected = expected / np.sqrt(8)
np.testing.assert_allclose(test, expected)
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 test_04_unitarity(self):
"""
control: unitarity checking (via dump)
Dump out processing data and use to check unitary evolution
"""
# Hadamard
H_d = sigmaz()
H_c = [sigmax()]
U_0 = identity(2)
U_targ = hadamard_transform(1)
n_ts = 1000
evo_time = 4
result = cpo.optimize_pulse_unitary(H_d, H_c, U_0, U_targ,
n_ts, evo_time,
fid_err_targ=1e-9,
init_pulse_type='LIN',
dyn_params={'dumping':'FULL'},
gen_stats=True)
# check dumps were generated
optim = result.optimizer
dyn = optim.dynamics
assert_(dyn.dump is not None, msg='dynamics dump not created')
# Use the dump to check unitarity of all propagators and evo_ops
dyn.unitarity_tol = 1e-14
nu_prop = 0
nu_fwd_evo = 0
nu_onto_evo = 0
for d in dyn.dump.evo_dumps:
for k in range(dyn.num_tslots):
if not dyn._is_unitary(d.prop[k]): nu_prop += 1
if not dyn._is_unitary(d.fwd_evo[k]): nu_fwd_evo += 1
if not dyn._is_unitary(d.onto_evo[k]): nu_onto_evo += 1
assert_(nu_prop==0,
msg="{} propagators found to be non-unitary".format(nu_prop))
assert_(nu_fwd_evo==0,
msg="{} fwd evo ops found to be non-unitary".format(
nu_fwd_evo))
assert_(nu_onto_evo==0,
msg="{} onto evo ops found to be non-unitary".format(
nu_onto_evo))
def test_01_5_unitary_hadamard_oo(self):
"""
control.pulseoptim: Hadamard gate with linear initial pulses (OO)
assert that goal is achieved and pulseoptim method achieves
same result as OO method
"""
# Hadamard
H_d = sigmaz()
H_c = [sigmax()]
U_0 = identity(2)
U_targ = hadamard_transform(1)
n_ts = 10
evo_time = 10
# Run the optimisation
optim = cpo.create_pulse_optimizer(H_d, H_c, U_0, U_targ,
n_ts, evo_time,
fid_err_targ=1e-10,
dyn_type='UNIT',
init_pulse_type='LIN',
gen_stats=True)
dyn = optim.dynamics
init_amps = optim.pulse_generator.gen_pulse().reshape([-1, 1])
dyn.initialize_controls(init_amps)
result_oo = optim.run_optimization()
# Run the pulseoptim func
result_po = cpo.optimize_pulse_unitary(H_d, H_c, U_0, U_targ,
n_ts, evo_time,
fid_err_targ=1e-10,
init_pulse_type='LIN',
gen_stats=True)
assert_almost_equal(result_oo.fid_err, result_po.fid_err, decimal=10,
err_msg="OO and pulseoptim methods produce "
"different results for Hadamard")
def test_01_3_unitary_hadamard_tau(self):
"""
control.pulseoptim: Hadamard gate with linear initial pulses (tau)
assert that goal is achieved
"""
# Hadamard
H_d = sigmaz()
H_c = [sigmax()]
U_0 = identity(2)
U_targ = hadamard_transform(1)
# Run the optimisation
#Try setting timeslots with tau array
tau = np.arange(1.0, 10.0, 1.0)
result = cpo.optimize_pulse_unitary(H_d, H_c, U_0, U_targ,
tau=tau,
fid_err_targ=1e-10,
init_pulse_type='LIN',
gen_stats=False)
assert_(result.goal_achieved, msg="Hadamard goal not achieved "
"(tau as timeslots). "
"Terminated due to: {}, with infidelity: {}".format(
result.termination_reason, result.fid_err))
import scipy.optimize
import qutip
from qutip.control import pulseoptim as cpo
from qutip.qip.algorithms import qft
from qutip.qip.operations.gates import hadamard_transform
import qutip.control.loadparams
_sx = qutip.sigmax()
_sy = qutip.sigmay()
_sz = qutip.sigmaz()
_sp = qutip.sigmap()
_sm = qutip.sigmam()
_si = qutip.identity(2)
_project_0 = qutip.basis(2, 0).proj()
_hadamard = hadamard_transform(1)
# We have a whole bunch of different physical systems we want to test the
# optimiser for, but the logic for testing them is largely the same. To avoid
# having to explicitly parametrise over five linked parameters repeatedly, we
# group them into a record type, so that all the optimisation functions can
# then simply be parametrised over a single argument.
#
# We supply `kwargs` as a property of the system because the initial pulse type
# and dynamics solver to use vary, especially if the system is unitary.
_System = collections.namedtuple('_System',
['system', 'controls', 'initial', 'target',
'kwargs'])
# Simple Hadamard gate.
_hadamard_kwargs = {'num_tslots': 10, 'evo_time': 10, 'gen_stats': True,
def had_mixture(x):
id_chan = to_choi(qeye(2))
S_eye = to_super(id_chan)
S_H = to_super(hadamard_transform())
return (1 - x) * S_eye + x * S_H
def test_12_time_dependent_ctrls(self):
"""
control.pulseoptim: Hadamard gate with fixed and time varying ctrls
assert that goal is achieved for both and that different control
pulses are produced (only) when they should be.
"""
# Hadamard
H_0 = sigmaz()
H_c = [sigmax()]
U_0 = identity(2)
U_targ = hadamard_transform(1)
n_ts = 20
evo_time = 10
# Run the optimisations
result_fixed = cpo.optimize_pulse_unitary(H_0, H_c, U_0, U_targ,
n_ts, evo_time,
fid_err_targ=1e-10,
init_pulse_type='LIN',
gen_stats=True)
assert_(result_fixed.goal_achieved,
msg="Fixed ctrls goal not achieved. "
"Terminated due to: {}, with infidelity: {}".format(
result_fixed.termination_reason, result_fixed.fid_err))
H_c_t = []
for k in range(n_ts):
H_c_t.append([sigmax()])
result_tdcs = cpo.optimize_pulse_unitary(H_0, H_c_t, U_0, U_targ,
n_ts, evo_time,
fid_err_targ=1e-10,
init_pulse_type='LIN',
gen_stats=True)
assert_(result_tdcs.goal_achieved,
msg="td same ctrl goal not achieved. "
"Terminated due to: {}, with infidelity: {}".format(
result_tdcs.termination_reason, result_tdcs.fid_err))
# Check fixed and same produced the same pulse
assert_almost_equal(result_fixed.final_amps, result_tdcs.final_amps,
decimal=9,
err_msg="same and fixed ctrls result in "
"different control pules")
H_c_t = []
for k in range(n_ts):
if k % 3 == 0:
H_c_t.append([sigmax()])
else:
H_c_t.append([identity(2)])
result_tdcv = cpo.optimize_pulse_unitary(H_0, H_c_t, U_0, U_targ,
n_ts, evo_time,
fid_err_targ=1e-10,
init_pulse_type='LIN',
gen_stats=True)
assert_(result_tdcv.goal_achieved,
msg="true td ctrls goal not achieved. "
"Terminated due to: {}, with infidelity: {}".format(
result_tdcv.termination_reason, result_tdcv.fid_err))
# Check that identity control tslots don't vary
for k in range(n_ts):
if k % 3 != 0:
assert_almost_equal(result_tdcv.initial_amps[k, 0],
result_tdcv.final_amps[k, 0],
decimal=9,
err_msg=("timeslot {} amps should remain "
"fixed").format(k))
def test_03_dumping(self):
"""
control: data dumping
Dump out processing data, check file counts
"""
self.setUp()
N_EXP_OPTIMDUMP_FILES = 10
N_EXP_DYNDUMP_FILES = 49
# Hadamard
H_d = sigmaz()
H_c = [sigmax()]
U_0 = identity(2)
U_targ = hadamard_transform(1)
n_ts = 1000
evo_time = 4
dump_folder = str(uuid.uuid4())
qtrl_dump_dir = os.path.expanduser(os.path.join('~', dump_folder))
self.tmp_dirs.append(qtrl_dump_dir)
optim_dump_dir = os.path.join(qtrl_dump_dir, 'optim')
dyn_dump_dir = os.path.join(qtrl_dump_dir, 'dyn')
result = cpo.optimize_pulse_unitary(H_d, H_c, U_0, U_targ,
n_ts, evo_time,
fid_err_targ=1e-9,
init_pulse_type='LIN',
optim_params={'dumping':'FULL', 'dump_to_file':True,
'dump_dir':optim_dump_dir},
dyn_params={'dumping':'FULL', 'dump_to_file':True,
'dump_dir':dyn_dump_dir},
gen_stats=True)
# check dumps were generated
optim = result.optimizer
dyn = optim.dynamics
assert_(optim.dump is not None, msg='optimizer dump not created')
assert_(dyn.dump is not None, msg='dynamics dump not created')
# Count files that were output
nfiles = len(os.listdir(optim.dump.dump_dir))
assert_(nfiles == N_EXP_OPTIMDUMP_FILES,
msg="{} optimizer dump files generated, {} expected".format(
nfiles, N_EXP_OPTIMDUMP_FILES))
nfiles = len(os.listdir(dyn.dump.dump_dir))
assert_(nfiles == N_EXP_DYNDUMP_FILES,
msg="{} dynamics dump files generated, {} expected".format(
nfiles, N_EXP_DYNDUMP_FILES))
# dump all to specific file stream
fpath = os.path.expanduser(os.path.join('~', str(uuid.uuid4())))
self.tmp_files.append(fpath)
with open(fpath, 'wb') as f:
optim.dump.writeout(f)
assert_(os.stat(fpath).st_size > 0,
msg="Nothing written to optimizer dump file")
fpath = os.path.expanduser(os.path.join('~', str(uuid.uuid4())))
self.tmp_files.append(fpath)
with open(fpath, 'wb') as f:
dyn.dump.writeout(f)
assert_(os.stat(fpath).st_size > 0,
msg="Nothing written to dynamics dump file")
self.tearDown()
def test_09_load_params(self):
"""
control.pulseoptim: Hadamard gate (loading config from file)
compare with result produced by pulseoptim method
"""
H_d = sigmaz()
H_c = sigmax()
U_0 = identity(2)
U_targ = hadamard_transform(1)
cfg = optimconfig.OptimConfig()
cfg.param_fname = "Hadamard_params.ini"
cfg.param_fpath = os.path.join(os.path.dirname(__file__),
cfg.param_fname)
cfg.pulse_type = "ZERO"
loadparams.load_parameters(cfg.param_fpath, config=cfg)
dyn = dynamics.DynamicsUnitary(cfg)
dyn.target = U_targ
dyn.initial = U_0
dyn.drift_dyn_gen = H_d
dyn.ctrl_dyn_gen = [H_c]
loadparams.load_parameters(cfg.param_fpath, dynamics=dyn)
dyn.init_timeslots()
n_ts = dyn.num_tslots
n_ctrls = dyn.num_ctrls
pgen = pulsegen.create_pulse_gen(pulse_type=cfg.pulse_type, dyn=dyn)
loadparams.load_parameters(cfg.param_fpath, pulsegen=pgen)
tc = termcond.TerminationConditions()
loadparams.load_parameters(cfg.param_fpath, term_conds=tc)
if cfg.optim_method == 'BFGS':
optim = optimizer.OptimizerBFGS(cfg, dyn)
elif cfg.optim_method == 'FMIN_L_BFGS_B':
optim = optimizer.OptimizerLBFGSB(cfg, dyn)
elif cfg.optim_method is None:
raise errors.UsageError("Optimisation algorithm must be specified "
"via 'optim_method' parameter")
else:
optim = optimizer.Optimizer(cfg, dyn)
optim.method = cfg.optim_method
loadparams.load_parameters(cfg.param_fpath, optim=optim)
sts = stats.Stats()
dyn.stats = sts
optim.stats = sts
optim.config = cfg
optim.dynamics = dyn
optim.pulse_generator = pgen
optim.termination_conditions = tc
init_amps = np.zeros([n_ts, n_ctrls])
for j in range(n_ctrls):
init_amps[:, j] = pgen.gen_pulse()
dyn.initialize_controls(init_amps)
result = optim.run_optimization()
result2 = cpo.optimize_pulse_unitary(H_d, list([H_c]), U_0, U_targ,
6, 6, fid_err_targ=1e-10,
init_pulse_type='LIN',
amp_lbound=-1.0, amp_ubound=1.0,
gen_stats=True)
assert_almost_equal(result.final_amps, result2.final_amps, decimal=5,
err_msg="Pulses do not match")
def test_06_lindbladian(self):
"""
control.pulseoptim: amplitude damping channel
Lindbladian dynamics
assert that fidelity error is below threshold
"""
Sx = sigmax()
Sz = sigmaz()
Si = identity(2)
Sd = Qobj(np.array([[0, 1], [0, 0]]))
Sm = Qobj(np.array([[0, 0], [1, 0]]))
Sd_m = Qobj(np.array([[1, 0], [0, 0]]))
gamma = 0.1
L0_Ad = gamma*(2*tensor(Sm, Sd.trans()) -
(tensor(Sd_m, Si) + tensor(Si, Sd_m.trans())))
LC_x = -1j*(tensor(Sx, Si) - tensor(Si, Sx))
LC_z = -1j*(tensor(Sz, Si) - tensor(Si, Sz))
drift = L0_Ad
ctrls = [LC_z, LC_x]
n_ctrls = len(ctrls)
initial = tensor(Si, Si)
had_gate = hadamard_transform(1)
target_DP = tensor(had_gate, had_gate)
n_ts = 10
evo_time = 5
result = cpo.optimize_pulse(drift, ctrls, initial, target_DP,
n_ts, evo_time,
fid_err_targ=1e-3,
max_iter=200,
init_pulse_type='LIN',
gen_stats=True)
assert_(result.fid_err < 0.1,
msg="Fidelity higher than expected")
# Repeat with Qobj propagation
result = cpo.optimize_pulse(drift, ctrls, initial, target_DP,
n_ts, evo_time,
fid_err_targ=1e-3,
max_iter=200,
init_pulse_type='LIN',
dyn_params={'oper_dtype':Qobj},
gen_stats=True)
assert_(result.fid_err < 0.1,
msg="Fidelity higher than expected (Qobj propagation)")
# Check same result is achieved using the create objects method
optim = cpo.create_pulse_optimizer(drift, ctrls,
initial, target_DP,
n_ts, evo_time,
fid_err_targ=1e-3,
init_pulse_type='LIN',
gen_stats=True)
dyn = optim.dynamics
p_gen = optim.pulse_generator
init_amps = np.zeros([n_ts, n_ctrls])
for j in range(n_ctrls):
init_amps[:, j] = p_gen.gen_pulse()
dyn.initialize_controls(init_amps)
# Check the exact gradient
func = optim.fid_err_func_wrapper
grad = optim.fid_err_grad_wrapper
x0 = dyn.ctrl_amps.flatten()
grad_diff = check_grad(func, grad, x0)
assert_almost_equal(grad_diff, 0.0, decimal=7,
err_msg="Frechet gradient outside tolerance")
result2 = optim.run_optimization()
assert_almost_equal(result.fid_err, result2.fid_err, decimal=3,
err_msg="Direct and indirect methods produce "
"different results for ADC")
