def make_init_pulse(self):
class_pulse = pulsegen.create_pulse_gen(
pulse_type=self.init_pulse_type, dyn=None, pulse_params=None)
class_pulse.num_tslots = self.n_tslot
init_pulse_prescale = class_pulse.gen_pulse()
pulse = init_pulse_prescale * self.power_max / max(init_pulse_prescale)
return pulse.reshape([self.n_tslot, 1])
def make_init_pulse(self):
init_pulse = zeros([2,self.n_tslot])
class_pulse = pulsegen.create_pulse_gen(pulse_type=self.init_pulse_type, dyn=None, pulse_params=None)
class_pulse.num_tslots = self.n_tslot
init_pulse_prescale = class_pulse.gen_pulse()
init_pulse[0] = init_pulse_prescale*self.power_max/max(init_pulse_prescale)
init_pulse[1] = init_pulse_prescale*self.power_max/max(init_pulse_prescale)
return init_pulse.T
def test_load_params(self):
"""
Optimise pulse for Hadamard gate by 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")
tc.fid_err_targ = 1e-3
tc.min_gradient_norm = 1e-10
tc.max_iter_total = 200
tc.max_wall_time_total = 30
tc.break_on_targ = True
optim = optimizer.OptimizerCrabFmin(cfg, dyn)
sts = stats.Stats()
dyn.stats = sts
optim.stats = sts
optim.config = cfg
optim.dynamics = dyn
optim.termination_conditions = tc
guess_pgen = pulsegen.create_pulse_gen('LIN', dyn)
init_amps = np.zeros([n_ts, n_ctrls])
optim.pulse_generator = []
for j in range(n_ctrls):
# Note that for CRAB each control must have its own pulse generator
# as the frequencies and coeffs values are stored in the pulsegen
pgen = pulsegen.PulseGenCrabFourier(dyn)
pgen.scaling = 0.1
# comment out the next line for no guess pulse
pgen.guess_pulse = guess_pgen.gen_pulse()
optim.pulse_generator.append(pgen)
init_amps[:, j] = pgen.gen_pulse()
dyn.initialize_controls(init_amps)
# *************************************************************
# File extension for output files
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")
optim = cpo.create_pulse_optimizer(H_d, H_c, U_0, U_targ, n_ts, evo_time,
fid_err_targ=fid_err_targ,
max_iter=max_iter, max_wall_time=max_wall_time,
alg='CRAB',
dyn_type='UNIT',
prop_type='DIAG',
fid_type='UNIT', fid_params={'phase_option':'PSU'},
log_level=log_level, gen_stats=True)
##Configure the pulses for each of the controls
dyn = optim.dynamics
# Control 1
crab_pgen = optim.pulse_generator[0]
# Start from a ramped pulse
guess_pgen = pulsegen.create_pulse_gen('LIN', dyn=dyn,
pulse_params={'scaling':3.0})
crab_pgen.guess_pulse = guess_pgen.gen_pulse()
crab_pgen.scaling = 0.0
# Add some higher frequency components
crab_pgen.num_coeffs = 5
# Control 2
crab_pgen = optim.pulse_generator[1]
# Apply a ramping pulse that will force the start and end to zero
ramp_pgen = pulsegen.create_pulse_gen('GAUSSIAN_EDGE', dyn=dyn,
pulse_params={'decay_time':evo_time/50.0})
crab_pgen.ramping_pulse = ramp_pgen.gen_pulse()
# Control 3
crab_pgen = optim.pulse_generator[2]
# Add bounds
def gen_optim_objects(cfg, verbosity=None):
"""
Generate the optimiser objects based on the configuration
Returns
-------
Optimizer
"""
if verbosity is None:
verbosity = cfg.verbosity
def printv(msg, verb_tresh=1):
if verbosity and verbosity >= verb_tresh:
print(msg)
dyn = dynamics.DynamicsUnitary(cfg)
dyn.dense_oper = True
dyn.memory_optimization = 1
# Number of timeslots for the pulse discretisation
dyn.num_tslots = 12
# For a range of evo_time based on job_idx (cfg.idx_opt='num_tslots')
# For a specific list of num_tslots values based on job_idx
dyn.num_tslots_list = []
# Or generate a range
dyn.st_num_tslots = 10
dyn.d_num_tslots = 5
dyn.num_num_tslots = 10
# Time allowed for the gate to evolve
dyn.evo_time = 10.0
# For a range of evo_time based on job_idx (cfg.idx_opt='evo_time'):
# For a specific list of evo_time values based on job_idx
dyn.evo_time_list = [7, 10, 12, 20]
# Or generate a range
dyn.st_evo_time = 0.2
dyn.d_evo_time = 0.2
dyn.num_evo_time = 50
# Any number of qubits >= 3 can choosen.
# However larger numbers take a lot of processing
dyn.num_qubits = 3
# Only CNOT implemented for target_type
dyn.target_type = 'CNOT'
# interact options: Ising|Heisenberg| or combinations of xyz
dyn.interact = 'Ising'
# topology options: chain|star|full|ring
dyn.topology = 'chain'
# ctrls_type options: combinations of xyz
dyn.ctrls_type = 'XY'
# If True then same coupling constant will be used for
# all interactions between qubit pairs
dyn.iso_coup = True
# Coupling strength for each interaction
# If a single float, then this will be used for all interactions
# (single float list will be converted to float)
# Otherwise
# If iso_coup=True then should be one list item per iteracting pair
# If iso_coup=False then should be blocks of list items per iteracting pair
# e.g. for 4 qubit Heisenberg chain:
# [g_0x, g_1x, g_2x, g_0y, g_1y, g_2y, g_0z, g_1z, g_2z]
# Note that the number of interacting pairs is n-1 for chain and star,
# n for ring, and n*(n-1)/2 for fully connected
dyn.coup_const = [1.0]
# Used to change the order of the qubits in the Hilbert space
# The two qubit gate will always be between 0 and 1,
# but the couplings will be permuted based on hspace_order
dyn.hspace_order = []
# When True the hspace_order will be automatically generated
# based on the other attributes
dyn.auto_hspace = False
# Separation of the first and second qubits in the coupling Hilbert space
# 0 implies adjacent, -1 implies random
dyn.hspace_01_sep = 0
# Index of the first qubit in the coupling Hilbert space
# -1 implies random
dyn.hspace_0_idx = 0
# dynamical decoup attribs
# These are the decoup amplitudes in the three axes
dyn.decoup_x = 0.0
dyn.decoup_y = 0.0
dyn.decoup_z = 0.0
# num_decoup_tslots = None means use the specific mask
# num_decoup_tslots = 0 use all
# num_decoup_tslots +ve is the number of tslots from start
# num_decoup_tslots -ve is the number of zeros at the end
dyn.num_decoup_tslots = 0
dyn.decoup_tslots = []
#dyn.decoup_tslot_mask = []
if hasattr(cfg, 'args') and isinstance(cfg.args, argparse.ArgumentParser):
parse_cl_args = True
else:
parse_cl_args = False
if cfg.use_param_file:
# load the dynamics parameters
# note these will overide those above if present in the file
printv("Loading dynamics parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, dynamics=dyn)
if parse_cl_args:
if cfg.args['num_qubits'] > 0:
dyn.num_qubits = cfg.args['num_qubits']
printv("num_qubits = {} from command line".format(dyn.num_qubits))
# If job_idx is given, then use it to calculate some other parameter
# (which supersedes all other settings)
if cfg.job_idx and len(cfg.args['idx_opt']) > 0:
cfg.ext_mp = True
opt_str = cfg.args['idx_opt'].lower()
if opt_str == 'evo_time':
printv("basing evo_time on job_idx... ")
num_evo_time = len(dyn.evo_time_list)
if num_evo_time > 0:
printv("...using evo_time_list")
# get the evo time from the list
T_idx = (cfg.job_idx - 1) % num_evo_time
dyn.evo_time = dyn.evo_time_list[T_idx]
else:
printv("...using start and increment")
# calculate the evo_time from job_idx
T_idx = (cfg.job_idx - 1) % dyn.num_evo_time
dyn.evo_time = (dyn.st_evo_time
+ dyn.d_evo_time*float(T_idx))
printv("evo_time={} for job idx {}".format(dyn.evo_time,
cfg.job_idx))
elif opt_str == 'num_tslots':
printv("basing num_tslots on job_idx... ")
num_nts = len(dyn.num_tslots_list)
if num_nts > 0:
printv("...using num_tslots_list")
nts_idx = (cfg.job_idx - 1) % num_nts
dyn.num_tslots = dyn.num_tslots_list[nts_idx]
else:
printv("...using start and increment")
# calculate the num_tslots from job_idx
nts_idx = (cfg.job_idx - 1) % dyn.num_num_tslots
dyn.num_tslots = (dyn.st_num_tslots
+ dyn.d_num_tslots*nts_idx)
printv("num_tslots={} for job idx {}".format(dyn.num_tslots,
cfg.job_idx))
else:
raise ValueError("No option for idx_opt '{}' "
"in command line".format(opt_str))
if cfg.args['evo_time'] > 0:
dyn.evo_time = cfg.args['evo_time']
printv("Using evo_time={} from command line".format(dyn.evo_time))
if cfg.args['evo_time_npi'] > 0:
dyn.evo_time = np.pi*cfg.args['evo_time_npi']
printv("Using evo_time={} from command line evo_time_npi".format(
dyn.evo_time))
if cfg.args['evo_time_npi_g0s'] > 0:
dyn.evo_time = np.pi*cfg.args['evo_time_npi_g0s']/dyn.coup_const[0]
printv("Using evo_time={} from command line "
"evo_time_npi_g0s".format(dyn.evo_time))
if cfg.args['num_tslots'] > 0:
dyn.num_tslots = cfg.args['num_tslots']
printv("Using num_tslots={} from command line".format(
dyn.num_tslots))
if cfg.args['mem_opt'] > 0:
dyn.memory_optimization = cfg.args['mem_opt']
printv("Using mem_opt={} from command line".format(
dyn.memory_optimization))
printv("evo_time={}".format(dyn.evo_time))
printv("num_tslots={}".format(dyn.num_tslots))
if dyn.num_tslots == 0:
dyn.num_tslots = dyn.num_qubits*4 + 8
printv("num_tslots calculated and set to be {}".format(dyn.num_tslots))
if len(dyn.coup_const) == 1:
dyn.coup_const = dyn.coup_const[0]
dyn.prop_computer = propcomp.PropCompFrechet(dyn)
if dyn.dense_oper:
dyn.oper_dtype = np.ndarray
else:
dyn.oper_dtype = Qobj
# Pulse optimisation termination conditions
# Create the TerminationConditions instance
tc = termcond.TerminationConditions()
# Target for the infidelity (1 - gate fidelity)
tc.fid_err_targ = 1e-3
# Sum of the gradients wrt optimisation parameters (timeslot amplitudes)
tc.min_gradient_norm = 1e-30
# Number of iterations of the algorithm
tc.max_iter = 2400
# Computation time (in seconds)
tc.max_wall_time = 120*60.0
# Relative change in fid_err between iterations
tc.accuracy_factor = 1e5
if cfg.use_param_file:
# load the termination condition parameters
# note these will overide those above if present in the file
printv("Loading termination condition parameters from {}".format(
cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, term_conds=tc)
if parse_cl_args:
if cfg.args['fid_err_targ'] > 0.0:
tc.fid_err_targ = cfg.args['fid_err_targ']
printv("fid_err_targ = {} from cmd line".format(tc.fid_err_targ))
if cfg.args['max_wall_time'] > 0.0:
tc.max_wall_time = cfg.args['max_wall_time']
printv("max_wall_time = {} from cmd line".format(tc.max_wall_time))
# Fidelity oomputer
ft = cfg.fid_type.lower()
if ft == 'pure_choi_local':
dyn.fid_computer = FidCompPureChoiLocal(dyn)
elif ft == 'pure_choi_global':
dyn.fid_computer = FidCompPureChoiGlobal(dyn)
elif ft == 'unit_global':
dyn.fid_computer = fidcomp.FidCompUnitary(dyn)
dyn.fid_computer.local = False
else:
raise errors.UsageError("Unknown fid type {}".format(cfg.fid_type))
fid_comp = dyn.fid_computer
# If True and optim dumping set to at least SUMMARY, then the
# global fid will be added to the summary (when using pure_choi_local)
fid_comp.dump_global_choi_fid = False
# This is the maximum precsion that sub-system fidelities are 'measured'
# see choi_closed_fidcomp.my_round for details
# Zero implies full machine precision
fid_comp.numer_acc = 0.0
# These next parameters are used in the automatic search for the numerical
# accuracy threshold
# If numer_acc_exact==false, then numer_acc, st_numer_acc, end_numer_acc
# will be treated as proportion of fid_err_targ (during config)
fid_comp.numer_acc_exact = False
fid_comp.st_numer_acc = 0.01
fid_comp.end_numer_acc = 0.2
# These proportions are used to determine the boundaries for the search
# They are proportions of the number number of successful repeats
# for the scenario.
fid_comp.success_prop_uthresh = 0.95
fid_comp.success_prop_lthresh = 0.01
if cfg.use_param_file:
# load the pulse generator parameters
# note these will overide those above if present in the file
printv("Loading fidcomp parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, obj=dyn.fid_computer,
section='fidcomp')
if parse_cl_args:
if cfg.args['numer_acc'] >= 0.0:
fid_comp.numer_acc = cfg.args['numer_acc']
printv("numer_acc = {} from cmd line".format(fid_comp.numer_acc))
if not fid_comp.numer_acc_exact:
fid_comp.numer_acc = round_sigfigs(
fid_comp.numer_acc*tc.fid_err_targ, 6)
fid_comp.st_numer_acc = round_sigfigs(
fid_comp.st_numer_acc*tc.fid_err_targ, 6)
fid_comp.end_numer_acc = round_sigfigs(
fid_comp.end_numer_acc*tc.fid_err_targ, 6)
# Pulse generator
p_gen = pulsegen.create_pulse_gen(pulse_type=cfg.p_type, dyn=dyn)
p_gen.all_ctrls_in_one = False
p_gen.lbound = cfg.amp_lbound
p_gen.ubound = cfg.amp_ubound
if cfg.use_param_file:
# load the pulse generator parameters
# note these will overide those above if present in the file
printv("Loading pulsegen parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, pulsegen=p_gen)
if not isinstance(p_gen, pulsegen.PulseGenCrab):
if not (np.isinf(cfg.amp_lbound) or np.isinf(cfg.amp_ubound)):
p_gen.scaling = cfg.amp_ubound - cfg.amp_lbound
p_gen.offset = (cfg.amp_ubound + cfg.amp_lbound) / 2.0
if parse_cl_args:
if cfg.args['pulse_scaling'] > 0.0:
p_gen.scaling = cfg.args['pulse_scaling']
printv("p_gen.scaling = {} from cmd line".format(p_gen.scaling))
# Create the Optimiser instance
if cfg.optim_method is None:
raise errors.UsageError("Optimisation method must be specified "
"via 'optim_method' parameter")
om = cfg.optim_method.lower()
if om == 'bfgs':
optim = optimizer.OptimizerBFGS(cfg, dyn)
elif om == 'lbfgsb':
optim = optimizer.OptimizerLBFGSB(cfg, dyn)
else:
# Assume that the optim_method is valid
optim = optimizer.Optimizer(cfg, dyn)
optim.method = cfg.optim_method
if cfg.verbosity > 1:
printv("Created optimiser of type {}".format(type(optim)), 2)
optim.config = cfg
optim.dynamics = dyn
optim.termination_conditions = tc
optim.pulse_generator = p_gen
optim.method_approach = 'DEF'
optim.dumping = 'SUMMARY'
if cfg.use_param_file:
# load the optimiser parameters
# note these will overide those above if present in the file
printv("Loading optimiser parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, optim=optim)
optim.termination_conditions = tc
if cfg.gen_stats:
# Create a stats object
# Note that stats object is optional
# if the Dynamics and Optimizer stats attribute is not set
# then no stats will be collected, which could improve performance
stats_type = 'standard'
try:
stats_type = cfg.stats_type.lower()
except:
pass
if stats_type == 'local':
sts = qsostats.StatsFidCompLocal()
else:
sts = stats.Stats()
dyn.stats = sts
optim.stats = sts
return optim
# Physical parameters
dyn.coupling1 = 0.3
dyn.coupling2 = 0.2
dyn.sqz = 0.5
dyn.rot = 1.0
# load the dynamics parameters
# note these will overide those above if present in the file
print("Loading dynamics parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, dynamics=dyn)
dyn.init_timeslots()
n_ts = dyn.num_tslots
# Create a pulse generator of the type specified
p_gen = pulsegen.create_pulse_gen(pulse_type=cfg.pulse_type, dyn=dyn)
p_gen.lbound = cfg.amp_lbound
p_gen.ubound = cfg.amp_ubound
# load the pulse generator parameters
# note these will overide those above if present in the file
print("Loading pulsegen parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, pulsegen=p_gen)
# Create the TerminationConditions instance
tc = termcond.TerminationConditions()
tc.fid_err_targ = 1e-3
tc.min_gradient_norm = 1e-10
tc.max_iter_total = 200
tc.max_wall_time_total = 30
tc.break_on_targ = True
def gen_config(param_fname='unctrlsympl_params.ini'):
"""
Generate the configuration for the oscillator
In general values from the parameter file overwrite those hardcoded
and these in turn are overwritten by cmdline args
Returns
-------
Optimizer
"""
parser = argparse.ArgumentParser(
description="Command line argument parser")
parser.add_argument('-p',
'--param_file',
type=str,
default="",
help="Parameters file name")
parser.add_argument('-o',
'--output_dir',
type=str,
default="",
help="output sub directory")
parser.add_argument('-j',
'--job_id',
type=int,
default=0,
help="Job id (from bsub)")
parser.add_argument('-i',
'--job_idx',
type=int,
default=0,
help="Job index (from bsub batch array)")
parser.add_argument('-I',
'--idx_opt',
type=str,
default='',
help="Job index option (what to do with it)")
#parser.add_argument('-S', '--job_size', type=int, default=0,
# help="Number of jobs in array")
parser.add_argument('-N',
'--num_qubits',
type=int,
default=0,
help="Number of qubits")
parser.add_argument('-u',
'--init_amps',
type=str,
default=0,
help="File name of initial amplitudes")
parser.add_argument('-m',
'--num_tslots',
type=int,
default=0,
help="Number of timeslots")
parser.add_argument('-T',
'--evo_time',
type=float,
default=0,
help="Total evolution time")
parser.add_argument('--evo_time_npi',
type=int,
default=0,
help="Total evolution time in mulitples of pi")
parser.add_argument('--evo_time_npi_g0s',
type=int,
default=0,
help="Total evolution time in mulitples of pi "
"scaled by first coupling constant")
parser.add_argument('-a',
'--numer_acc',
type=float,
default=-1.0,
help="Numerical accuracy")
parser.add_argument('-e',
'--fid_err_targ',
type=float,
default=0.0,
help="Fidelity error target")
parser.add_argument('-n',
'--num_cpus',
type=int,
default=0,
help="Fidelity error target")
parser.add_argument('-M',
'--mp_opt',
type=str,
default='',
help="Multiprocessing option")
parser.add_argument('--mem_opt',
type=int,
default=0,
help="Memory optimising level")
parser.add_argument('--pulse_scaling',
type=int,
default=0.0,
help="Initial pulse scaling")
parser.add_argument('--max_wall_time',
type=int,
default=0.0,
help="Maximum simulation wall time")
args = vars(parser.parse_args())
cfg = optimconfig.OptimConfig()
cfg.log_level = log_level
cfg.use_param_file = True
if len(args['param_file']) > 0:
param_fname = args['param_file']
cfg.param_fpath = os.path.join(os.getcwd(), param_fname)
if not os.path.isfile(cfg.param_fpath):
raise ValueError(
"Commandline argument parameter "
"file name '{}' does not exist.".format(param_fname))
else:
print("Parameters will be read from:\n{}".format(cfg.param_fpath))
elif os.path.abspath(param_fname):
cfg.param_fname = os.path.basename(param_fname)
cfg.param_fpath = param_fname
param_fname = cfg.param_fname
else:
cfg.param_fpath = os.path.join(os.getcwd(), param_fname)
if (not os.path.isfile(cfg.param_fpath)):
print("Default parameter file {} not present. "
"Using defaults in code.").format(param_fname)
cfg.use_param_file = False
cfg.param_fname = param_fname
# Script operational parameters
cfg.job_id = 0
cfg.job_idx = 0
cfg.verbosity = 0
cfg.output_dir = None
cfg.output_base_name = 'general'
cfg.double_print = True
cfg.output_files = True
cfg.plot_result = False
cfg.plot_file_type = 'PNG'
cfg.save_plots = False
cfg.save_results = True
cfg.data_file_ext = "txt"
cfg.out_file_ext = "txt"
# Optimizer config
cfg.optim_method = 'LBFGSB'
cfg.p_type = 'RND'
cfg.check_grad = False
cfg.gen_stats = True
cfg.stats_type = 'standard'
cfg.report_stats = True
cfg.save_initial_amps = False
cfg.save_final_amps = False
cfg.fid_type = 'pure_choi_local'
cfg.amp_lbound = -np.Inf
cfg.amp_ubound = np.Inf
cfg.num_reps = 2
cfg.num_cpus = 1
cfg.mp_opt = ""
cfg.max_mp_scens = np.inf
cfg.ext_mp = False
cfg.num_threads = 1
if cfg.use_param_file:
# load the config parameters
# note these will overide those above if present in the file
print("Loading config parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, config=cfg)
# override with command line params (if any)
if args['num_qubits'] > 0:
cfg.num_qubits = args['num_qubits']
print("Using num_qubits={} from command line".format(cfg.num_qubits))
if len(args['output_dir']) > 0:
cfg.output_dir = args['output_dir']
print("Using output_dir '{}' from command line".format(cfg.output_dir))
if args['num_cpus'] > 0:
cfg.num_cpus = args['num_cpus']
print("Using num_cpus={} from command line".format(cfg.num_cpus))
if len(args['mp_opt']) > 0:
cfg.mp_opt = args['mp_opt']
print("Using mp_opt={} from command line".format(cfg.mp_opt))
cfg.plot_file_ext = cfg.plot_file_type.lower()
if args['job_id'] > 0:
cfg.job_id = args['job_id']
if cfg.job_id:
print("Processing job ID: {}".format(cfg.job_id))
if args['job_idx'] > 0:
cfg.job_idx = args['job_idx']
if cfg.job_idx:
print("Processing job array index: {}".format(cfg.job_idx))
cfg.out_file_ext = "{}.{}.{}".format(cfg.job_id, cfg.job_idx,
cfg.data_file_ext)
cfg.plot_file_ext = "{}.{}.{}".format(cfg.job_id, cfg.job_idx,
cfg.plot_file_type)
else:
cfg.out_file_ext = "{}.{}".format(cfg.job_id, cfg.data_file_ext)
cfg.plot_file_ext = "{}.{}".format(cfg.job_id, cfg.plot_file_type)
logger.setLevel(cfg.log_level)
if not cfg.output_dir:
cfg.output_dir = cfg.target_id_text
dyn = dynamics.DynamicsUnitary(cfg)
dyn.dense_oper = True
dyn.num_tslots_list = []
dyn.st_num_tslots = 10
dyn.d_num_tslots = 5
dyn.num_num_tslots = 10
dyn.evo_time = 10.0
# For a specific list of evo_time values based on job_idx
dyn.evo_time_list = [7, 10, 12, 20]
# For a range of evo_time based on job_idx (idx_opt='evo_time')
# Start evo_time
dyn.st_evo_time = 0.2
# Evo time step size
dyn.d_evo_time = 0.2
# Number of evo_time
dyn.num_evo_time = 50
dyn.num_qubits = 6
dyn.target_type = 'CNOT'
dyn.interact = 'Ising'
dyn.topology = 'chain'
dyn.ctrls_type = 'XY'
dyn.coup_const = [1.0]
# Used to change the order of the qubits in the Hilbert space
# so that the two qubit gate will always be between 0 and 1,
# but the couplings will be permuted based on hspace_order
dyn.hspace_order = []
# When True the hspace_order will be automatically generated
# based on the other attributes
dyn.auto_hspace = False
# Separation of the first and second qubits in the coupling Hilbert space
# 0 implies adjacent, -1 implies random
dyn.hspace_01_sep = 0
# Index of the first qubit in the coupling Hilbert space
# -1 implies random
dyn.hspace_0_idx = 0
# If True then same coupling constant will be used for
# all interactions between qubit pairs
dyn.iso_coup = True
# dynamical decoup attribs
# These are the decoup amplitudes in the three axes
dyn.decoup_x = 0.0
dyn.decoup_y = 0.0
dyn.decoup_z = 0.0
# num_decoup_tslots = None means use the specific mask
# num_decoup_tslots = 0 use all
# num_decoup_tslots +ve is the number of tslots from start
# num_decoup_tslots -ve is the number of zeros at the end
dyn.num_decoup_tslots = 0
dyn.decoup_tslots = []
#dyn.decoup_tslot_mask = []
if cfg.use_param_file:
# load the dynamics parameters
# note these will overide those above if present in the file
print("Loading dynamics parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, dynamics=dyn)
if args['num_qubits'] > 0:
dyn.num_qubits = args['num_qubits']
print("num_qubits = {} from command line".format(dyn.num_qubits))
# If job_idx is given, then use it to calculate some other parameter
# (which supersedes all other settings)
if cfg.job_idx and len(args['idx_opt']) > 0:
cfg.ext_mp = True
opt_str = args['idx_opt'].lower()
if opt_str == 'evo_time':
print("basing evo_time on job_idx... ")
num_evo_time = len(dyn.evo_time_list)
if num_evo_time > 0:
print("...using evo_time_list")
# get the evo time from the list
T_idx = (cfg.job_idx - 1) % num_evo_time
dyn.evo_time = dyn.evo_time_list[T_idx]
else:
print("...using start and increment")
# calculate the evo_time from job_idx
T_idx = (cfg.job_idx - 1) % dyn.num_evo_time
dyn.evo_time = dyn.st_evo_time + dyn.d_evo_time * float(T_idx)
print("evo_time={} for job idx {}".format(dyn.evo_time,
cfg.job_idx))
elif opt_str == 'num_tslots':
print("basing num_tslots on job_idx... ")
num_nts = len(dyn.num_tslots_list)
if num_nts > 0:
print("...using num_tslots_list")
nts_idx = (cfg.job_idx - 1) % num_nts
dyn.num_tslots = dyn.num_tslots_list[nts_idx]
else:
print("...using start and increment")
# calculate the num_tslots from job_idx
nts_idx = (cfg.job_idx - 1) % dyn.num_num_tslots
dyn.num_tslots = dyn.st_num_tslots + dyn.d_num_tslots * nts_idx
print("num_tslots={} for job idx {}".format(
dyn.num_tslots, cfg.job_idx))
else:
raise ValueError("No option for idx_opt '{}' "
"in command line".format(opt_str))
if args['evo_time'] > 0:
dyn.evo_time = args['evo_time']
print("Using evo_time={} from command line".format(dyn.evo_time))
if args['evo_time_npi'] > 0:
dyn.evo_time = np.pi * args['evo_time_npi']
print("Using evo_time={} from command line evo_time_npi".format(
dyn.evo_time))
if args['evo_time_npi_g0s'] > 0:
dyn.evo_time = np.pi * args['evo_time_npi_g0s'] / dyn.coup_const[0]
print("Using evo_time={} from command line evo_time_npi_g0s".format(
dyn.evo_time))
if args['num_tslots'] > 0:
dyn.num_tslots = args['num_tslots']
print("Using num_tslots={} from command line".format(dyn.num_tslots))
if args['mem_opt'] > 0:
dyn.memory_optimization = args['mem_opt']
print("Using mem_opt={} from command line".format(
dyn.memory_optimization))
print("evo_time={}".format(dyn.evo_time))
print("num_tslots={}".format(dyn.num_tslots))
if dyn.num_tslots == 0:
dyn.num_tslots = dyn.num_qubits * 4 + 8
print("num_tslots calculated and set to be {}".format(dyn.num_tslots))
if len(dyn.coup_const) == 1:
dyn.coup_const = dyn.coup_const[0]
dyn.prop_computer = propcomp.PropCompFrechet(dyn)
if dyn.dense_oper:
dyn.oper_dtype = np.ndarray
else:
dyn.oper_dtype = Qobj
# Create the TerminationConditions instance
tc = termcond.TerminationConditions()
tc.fid_err_targ = 1e-3
tc.min_gradient_norm = 1e-30
tc.max_iter = 2400
tc.max_wall_time = 120 * 60.0
tc.accuracy_factor = 1e5
if cfg.use_param_file:
# load the termination condition parameters
# note these will overide those above if present in the file
print("Loading termination condition parameters from {}".format(
cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, term_conds=tc)
if args['fid_err_targ'] > 0.0:
tc.fid_err_targ = args['fid_err_targ']
print("fid_err_targ = {} from command line".format(tc.fid_err_targ))
if args['max_wall_time'] > 0.0:
tc.max_wall_time = args['max_wall_time']
print("max_wall_time = {} from command line".format(tc.max_wall_time))
# Fidelity oomputer
ft = cfg.fid_type.lower()
if ft == 'pure_choi_local':
dyn.fid_computer = FidCompPureChoiLocal(dyn)
elif ft == 'pure_choi_global':
dyn.fid_computer = FidCompPureChoiGlobal(dyn)
elif ft == 'unit_global':
dyn.fid_computer = fidcomp.FidCompUnitary(dyn)
dyn.fid_computer.local = False
else:
raise errors.UsageError("Unknown fid type {}".format(cfg.fid_type))
fid_comp = dyn.fid_computer
fid_comp.numer_acc = 0.0
fid_comp.numer_acc_exact = False
fid_comp.st_numer_acc = 0.01
fid_comp.end_numer_acc = 0.2
fid_comp.success_prop_uthresh = 0.95
fid_comp.success_prop_lthresh = 0.01
if cfg.use_param_file:
# load the pulse generator parameters
# note these will overide those above if present in the file
print("Loading fidcomp parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath,
obj=dyn.fid_computer,
section='fidcomp')
if args['numer_acc'] >= 0.0:
fid_comp.numer_acc = args['numer_acc']
print("numer_acc = {} from command line".format(fid_comp.numer_acc))
if not fid_comp.numer_acc_exact:
fid_comp.numer_acc = round_sigfigs(
fid_comp.numer_acc * tc.fid_err_targ, 6)
fid_comp.st_numer_acc = round_sigfigs(
fid_comp.st_numer_acc * tc.fid_err_targ, 6)
fid_comp.end_numer_acc = round_sigfigs(
fid_comp.end_numer_acc * tc.fid_err_targ, 6)
# Pulse generator
p_gen = pulsegen.create_pulse_gen(pulse_type=cfg.p_type, dyn=dyn)
p_gen.all_ctrls_in_one = False
p_gen.lbound = cfg.amp_lbound
p_gen.ubound = cfg.amp_ubound
if cfg.use_param_file:
# load the pulse generator parameters
# note these will overide those above if present in the file
print("Loading pulsegen parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, pulsegen=p_gen)
if not isinstance(p_gen, pulsegen.PulseGenCrab):
if not (np.isinf(cfg.amp_lbound) or np.isinf(cfg.amp_ubound)):
p_gen.scaling = cfg.amp_ubound - cfg.amp_lbound
p_gen.offset = (cfg.amp_ubound + cfg.amp_lbound) / 2.0
if args['pulse_scaling'] > 0.0:
p_gen.scaling = args['pulse_scaling']
print("p_gen.scaling = {} from command line".format(p_gen.scaling))
# Create the Optimiser instance
if cfg.optim_method is None:
raise errors.UsageError("Optimisation method must be specified "
"via 'optim_method' parameter")
om = cfg.optim_method.lower()
if om == 'bfgs':
optim = optimizer.OptimizerBFGS(cfg, dyn)
elif om == 'lbfgsb':
optim = optimizer.OptimizerLBFGSB(cfg, dyn)
else:
# Assume that the optim_method is valid
optim = optimizer.Optimizer(cfg, dyn)
optim.method = cfg.optim_method
if cfg.verbosity > 1:
print("Created optimiser of type {}".format(type(optim)))
optim.config = cfg
optim.dynamics = dyn
optim.termination_conditions = tc
optim.pulse_generator = p_gen
optim.method_approach = 'DEF'
optim.dumping = 'SUMMARY'
if cfg.use_param_file:
# load the optimiser parameters
# note these will overide those above if present in the file
print("Loading optimiser parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, optim=optim)
optim.termination_conditions = tc
if cfg.gen_stats:
# Create a stats object
# Note that stats object is optional
# if the Dynamics and Optimizer stats attribute is not set
# then no stats will be collected, which could improve performance
stats_type = 'standard'
try:
stats_type = cfg.stats_type.lower()
except:
pass
if stats_type == 'local':
sts = qsostats.StatsFidCompLocal()
else:
sts = stats.Stats()
dyn.stats = sts
optim.stats = sts
# ****************************************************************
# Define the physics of the problem
print("Configuring drift...")
#The 4 below are all Qobj
# Sx = sigmax()
# Sy = sigmay()
# Sz = sigmaz()
# Si = identity(2)
nq = dyn.num_qubits
# ***** Drift *****
print("... for {} qubits".format(nq))
H_d = qso.get_drift(dyn)
print("Drift dims {}".format(H_d.dims))
# Normalise based on ising chain
H_d_ising_chain = qso.get_drift(dyn,
topology='chain',
interact='ising',
coup_const=1.0)
norm_fact = H_d_ising_chain.norm() / H_d.norm()
print("Normalising drift with factor {}".format(norm_fact))
H_d = H_d * norm_fact
# **** Controls ****
H_c, Sx_cidx, Sy_cidx, Sz_cidx = qso.get_ctrls(dyn)
n_ctrls = len(H_c)
#t0 evo
U_0 = qso.get_initial_op(dyn)
#*** Target ****
U_targ, U_local_targs = qso.get_target(dyn)
sub_dims = []
for k in range(len(U_local_targs)):
sub_dims.append(U_local_targs[k].dims[0][0])
#Enforcing all dimensions are correct for the qBranch code
#Not all of these are needed, but this is safer
for k in range(len(H_c)):
H_c[k].dims = [sub_dims, sub_dims]
H_d.dims = [sub_dims, sub_dims]
U_0.dims = [sub_dims, sub_dims]
U_targ.dims = [sub_dims, sub_dims]
dyn.drift_dyn_gen = H_d
dyn.ctrl_dyn_gen = H_c
dyn.initial = U_0
dyn.target = U_targ
# These are the indexes to the controls on the three axes
dyn.Sx_cidx = Sx_cidx
dyn.Sy_cidx = Sy_cidx
dyn.Sz_cidx = Sz_cidx
fid_comp.U_local_targs = U_local_targs
fid_comp.sub_dims = sub_dims
fid_comp.num_sub_sys = len(U_local_targs)
print("Num acc: {}".format(fid_comp.numer_acc))
return optim
def optimize(self):
frequency_grape = self.D0 + self.AN #-C_known[0]
#---------- 作成したいSTATE ----------#
n_goal = len(self.target_list)
n_tslot = int(np.round(self.pulse_time / self.t_div))
permin = -self.permax
rate_hem = 2 #触らない
freq_cut = 10 #触らない
class_pulse = pulsegen.create_pulse_gen(pulse_type='RNDFOURIER',
dyn=None,
pulse_params=None)
class_pulse.num_tslots = n_tslot
#class_pulse.num_ctrl = 1
init_pulse_prescale = class_pulse.gen_pulse()
init_pulse = init_pulse_prescale * self.permax / max(
init_pulse_prescale)
#init_pulse = 1/pulse_time/2.0*(sin(2*pi*(AN)*linspace(t_div,pulse_time,n_tslot)))
t_hem = self.pulse_time * rate_hem
n_hem = int(round(t_hem / self.t_div))
f_list = linspace(0, 1.0 / self.t_div, n_tslot + n_hem * 2)
Φ1_j = np.zeros(n_tslot)
Φ2_j = np.zeros(n_tslot)
"""
Create and return a pulse generator object matching the given type.
The pulse generators each produce a different type of pulse,
see the gen_pulse function description for details.
These are the random pulse options:
RND - Independent random value in each timeslot
RNDFOURIER - Fourier series with random coefficients
RNDWAVES - Summation of random waves
RNDWALK1 - Random change in amplitude each timeslot
RNDWALK2 - Random change in amp gradient each timeslot
These are the other non-periodic options:
LIN - Linear, i.e. contant gradient over the time
ZERO - special case of the LIN pulse, where the gradient is 0
These are the periodic options
SINE - Sine wave
SQUARE - Square wave
SAW - Saw tooth wave
TRIANGLE - Triangular wave
If a Dynamics object is passed in then this is used in instantiate
the PulseGen, meaning that some timeslot and amplitude properties
are copied over.
"""
def callback(self, pulse, f_list, t_hem, n_hem, n_tslot, freq_cut,
permin):
#print("========== Before FFT ==========") 描画する時はコメントアウトしない
"""
plt.plot(pulse)
plt.grid(b="on")
plt.show()
"""
pulse_hem_fft = hstack((zeros(n_hem), pulse, zeros(n_hem)))
t_list = linspace(self.t_div, self.pulse_time + t_hem * 2,
n_tslot + n_hem * 2)
FFT = np.fft.fft(pulse_hem_fft) / (n_tslot + n_hem * 2)
FFT_cut = hstack(
(FFT[0:int((n_tslot + n_hem * 2) /
(1 / self.t_div / freq_cut))],
zeros(
len(FFT) - int((n_tslot + n_hem * 2) /
(1 / self.t_div / freq_cut))))) * 2
pulse_hem = np.fft.fft(FFT_cut)[::-1].real
pulse = pulse_hem[n_hem:len(pulse_hem) - n_hem]
"""描画したいときはコメントアウトしない
plt.plot(f_list,FFT)
plt.plot(f_list,FFT_cut)
plt.xlim(0,freq_cut)
plt.grid(b="on")
plt.show()
pulse = np.clip(pulse,permin,self.permax)
print("========== After FFT ==========")
plt.plot(pulse)
plt.grid(b="on")
plt.show()
"""
return pulse
#=============================================== GRAPE!!! ===============================================#
number_C_known = len(self.C_known)
number_H = len(self.C_inhomo)
II_C_known = Qobj(qeye(2**number_C_known),
dims=[
list(2 * ones(number_C_known)),
list(2 * ones(number_C_known))
])
HhfCz_inhomo = 0
if number_C_known == 0:
for i in range(number_H):
HhfCz_inhomo = HhfCz_inhomo - self.C_inhomo[i] * tensor(
ket2dm(basis(number_H, i)), Sz, III, sigz)
else:
for i in range(number_H):
HhfCz_inhomo = HhfCz_inhomo - self.C_inhomo[i] * tensor(
ket2dm(basis(number_H, i)), Sz, III, II_C_known, sigz)
if number_H == 0:
II_inhomo = Qobj([[1]])
else:
II_inhomo = qeye(number_H)
H0 = defH0(self.D0, self.QN, self.AN, self.C_known, self.Bz)
Hdrift = matrix((tensor(II_inhomo, H0, II) + HhfCz_inhomo).full())
Hdrive1 = matrix(
tensor(II_inhomo, Hxdrive(1, 0, 0, 0, self.theta), III, II_C_known,
II).full())
Hdrive2 = matrix(
tensor(II_inhomo, Hxdrive(0, 1, 0, 0, self.theta), III, II_C_known,
II).full())
Hdetuning = matrix(tensor(II_inhomo, Szz, III, II_C_known, II).full())
U0 = matrix(tensor(II_inhomo, III, III, II_C_known, II).full())
for i in range(n_goal):
if number_H == 0:
self.state_init[i] = matrix(
tensor(II_inhomo, self.state_init[i], II / 2.0).full())
self.state_goal[i] = matrix(
tensor(II_inhomo, self.state_goal[i], II / 2.0).full())
else:
self.state_init[i] = matrix(
tensor(II_inhomo / number_H, self.state_init[i],
II / 2.0).full())
self.state_goal[i] = matrix(
tensor(II_inhomo / number_H, self.state_goal[i],
II / 2.0).full())
"""
weighting_value_targ = []
for i in range(n_goal):
weighting_value_targ = weighting_value_targ+[weighting_value[target_list[i]]]
"""
bounds = [(permin, self.permax)] * n_tslot
pulse = init_pulse
state_init = self.state_init
t_div = self.t_div
state_goal = self.state_goal
weighting_value = self.weighting_value
def grape_fid_err(pulse):
U_b = U0
fid_sum = []
for i in range(n_goal):
for j in range(n_tslot):
U_b = matrix(
sclin.expm(-2 * pi * 1j *
(Hdrift - (frequency_grape) * Hdetuning +
pulse[j] * Hdrive1) * self.t_div)) * U_b
state_final = U_b * state_init[i] * conj(U_b.T)
state_final_qobj = Qobj(state_final)
state_goal_qobj = Qobj(state_goal[i])
fid_sum = fid_sum + [
fidelity(state_final_qobj, state_goal_qobj) *
weighting_value[i]
]
fid_ave = sum(fid_sum) / sum(weighting_value)
#print("fid = "+str(fid_ave)) 描画する時はコメントアウトしない
return 1 - fid_ave
def grape_fid_err_grad(pulse):
U_rho_j = []
U_lamda_j = []
fid_err_grad_ave = []
U_rho = U0
U_lamda = U0
for j in range(n_tslot):
U_rho = matrix(
sclin.expm(-2 * pi * 1j *
(Hdrift - (frequency_grape) * Hdetuning +
pulse[j] * Hdrive1) * t_div)) * U_rho
U_lamda = conj(
matrix(
sclin.expm(-2 * pi * 1j *
(Hdrift - (frequency_grape) * Hdetuning +
pulse[n_tslot - j - 1] * Hdrive1) *
t_div)).T) * U_lamda
U_rho_j = U_rho_j + [U_rho]
U_lamda_j = U_lamda_j + [U_lamda]
for j in range(n_tslot):
fid_err_grad_j_sum = []
for k in range(n_goal):
lamda = (U_lamda_j[n_tslot - j -
2]) * state_goal[k] * conj(
U_lamda_j[n_tslot - j - 2].T)
state_j = U_rho_j[j] * state_init[k] * conj(U_rho_j[j].T)
fid_err_grad_j = np.real(
1j * 2 * pi * t_div *
trace(lamda * (Hdrive1 * state_j - state_j * Hdrive1)))
fid_err_grad_j_sum = fid_err_grad_j_sum + [
fid_err_grad_j * weighting_value[k]
]
fid_err_grad_ave = fid_err_grad_ave + [
sum(fid_err_grad_j_sum) / sum(weighting_value)
]
return array(fid_err_grad_ave)
for i in range(2):
pulse = callback(self, pulse, f_list, t_hem, n_hem, n_tslot,
freq_cut, permin)
result = sp_min.minimize(grape_fid_err,pulse,method="L-BFGS-B",jac=grape_fid_err_grad,bounds=bounds,\
options={'disp': None, 'maxcor': 10, 'ftol': 2.220446049250313e-09, 'gtol': 1e-05, 'eps': 1e-9, 'maxfun': 15000, 'maxiter': 1500, 'iprint': -1, 'maxls': 20})#,tol=1e-2)
pulse = result.x
Ω1_j = pulse #ラビ周波数の配列
Ω2_j = np.zeros(n_tslot)
return Φ1_j, Ω1_j
"""
def create_pulse_optimizer(drift,
ctrls,
initial,
target,
num_tslots=None,
evo_time=None,
tau=None,
amp_lbound=-np.Inf,
amp_ubound=np.Inf,
fid_err_targ=1e-10,
min_grad=1e-10,
max_iter=500,
max_wall_time=180,
optim_alg='LBFGSB',
max_metric_corr=10,
accuracy_factor=1e7,
dyn_type='GEN_MAT',
prop_type='DEF',
fid_type='DEF',
phase_option=None,
fid_err_scale_factor=None,
amp_update_mode='ALL',
init_pulse_type='RND',
pulse_scaling=1.0,
pulse_offset=0.0,
log_level=logging.NOTSET,
gen_stats=False):
"""
Generate the objects of the appropriate subclasses
required for the pulse optmisation based on the parameters given
Note this method may be preferable to calling optimize_pulse
if more detailed configuration is required before running the
optmisation algorthim, or the algorithm will be run many times,
for instances when trying to finding global the optimum or
minimum time optimisation
Parameters
----------
drift : Qobj
the underlying dynamics generator of the system
ctrls : List of Qobj
a list of control dynamics generators. These are scaled by
the amplitudes to alter the overall dynamics
initial : Qobj
starting point for the evolution.
Typically the identity matrix
target : Qobj
target transformation, e.g. gate or state, for the time evolution
num_tslots : integer or None
number of timeslots.
None implies that timeslots will be given in the tau array
evo_time : float or None
total time for the evolution
None implies that timeslots will be given in the tau array
tau : array[num_tslots] of floats or None
durations for the timeslots.
if this is given then num_tslots and evo_time are dervived
from it
None implies that timeslot durations will be equal and
calculated as evo_time/num_tslots
amp_lbound : float or list of floats
lower boundaries for the control amplitudes
Can be a scalar value applied to all controls
or a list of bounds for each control
amp_ubound : float or list of floats
upper boundaries for the control amplitudes
Can be a scalar value applied to all controls
or a list of bounds for each control
fid_err_targ : float
Fidelity error target. Pulse optimisation will
terminate when the fidelity error falls below this value
mim_grad : float
Minimum gradient. When the sum of the squares of the
gradients wrt to the control amplitudes falls below this
value, the optimisation terminates, assuming local minima
max_iter : integer
Maximum number of iterations of the optimisation algorithm
max_wall_time : float
Maximum allowed elapsed time for the optimisation algorithm
optim_alg : string
Multi-variable optimisation algorithm
options are BFGS, LBFGSB
(see Optimizer classes for details)
max_metric_corr : integer
The maximum number of variable metric corrections used to define
the limited memory matrix. That is the number of previous
gradient values that are used to approximate the Hessian
see the scipy.optimize.fmin_l_bfgs_b documentation for description
of m argument
(used only in L-BFGS-B)
accuracy_factor : float
Determines the accuracy of the result.
Typical values for accuracy_factor are: 1e12 for low accuracy;
1e7 for moderate accuracy; 10.0 for extremely high accuracy
scipy.optimize.fmin_l_bfgs_b factr argument.
(used only in L-BFGS-B)
dyn_type : string
Dynamics type, i.e. the type of matrix used to describe
the dynamics. Options are UNIT, GEN_MAT, SYMPL
(see Dynamics classes for details)
prop_type : string
Propagator type i.e. the method used to calculate the
propagtors and propagtor gradient for each timeslot
options are DEF, APPROX, DIAG, FRECHET, AUG_MAT
DEF will use the default for the specific dyn_type
(see PropagatorComputer classes for details)
fid_type : string
Fidelity error (and fidelity error gradient) computation method
Options are DEF, UNIT, TRACEDIFF, TD_APPROX
DEF will use the default for the specific dyn_type
(See FideliyComputer classes for details)
phase_option : string
determines how global phase is treated in fidelity
calculations (fid_type='UNIT' only). Options:
PSU - global phase ignored
SU - global phase included
fid_err_scale_factor : float
(used in TRACEDIFF FidelityComputer and subclasses only)
The fidelity error calculated is of some arbitary scale. This
factor can be used to scale the fidelity error such that it may
represent some physical measure
If None is given then it is caculated as 1/2N, where N
is the dimension of the drift.
amp_update_mode : string
determines whether propagators are calculated
Options: DEF, ALL, DYNAMIC (needs work)
DEF will use the default for the specific dyn_type
(See TimeslotComputer classes for details)
init_pulse_type : string
type / shape of pulse(s) used to initialise the
the control amplitudes. Options include:
RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW
(see PulseGen classes for details)
pulse_scaling : float
Linear scale factor for generated pulses
By default initial pulses are generated with amplitudes in the
range (-1.0, 1.0). These will be scaled by this parameter
pulse_offset : float
Line offset for the pulse. That is this value will be added
to any initial pulses generated.
log_level : integer
level of messaging output from the logger.
Options are attributes of qutip.logging,
in decreasing levels of messaging, are:
DEBUG_INTENSE, DEBUG_VERBOSE, DEBUG, INFO, WARN, ERROR, CRITICAL
Anything WARN or above is effectively 'quiet' execution,
assuming everything runs as expected.
The default NOTSET implies that the level will be taken from
the QuTiP settings file, which by default is WARN
Note value should be set using set_log_level
gen_stats : boolean
if set to True then statistics for the optimisation
run will be generated - accessible through attributes
of the stats object
Returns
-------
Instance of an Optimizer, through which the
Config, Dynamics, PulseGen, and TerminationConditions objects
can be accessed as attributes.
The PropagatorComputer, FidelityComputer and TimeslotComputer objects
can be accessed as attributes of the Dynamics object, e.g.
optimizer.dynamics.fid_computer
The optimisation can be run through the optimizer.run_optimization
"""
# check parameters
if not isinstance(drift, Qobj):
raise TypeError("drift must be a Qobj")
else:
drift = drift.full()
if not isinstance(ctrls, (list, tuple)):
raise TypeError("ctrls should be a list of Qobj")
else:
j = 0
for ctrl in ctrls:
if not isinstance(ctrl, Qobj):
raise TypeError("ctrls should be a list of Qobj")
else:
ctrls[j] = ctrl.full()
j += 1
if not isinstance(initial, Qobj):
raise TypeError("initial must be a Qobj")
else:
initial = initial.full()
if not isinstance(target, Qobj):
raise TypeError("target must be a Qobj")
else:
target = target.full()
cfg = optimconfig.OptimConfig()
cfg.optim_alg = optim_alg
cfg.max_metric_corr = max_metric_corr
cfg.accuracy_factor = accuracy_factor
cfg.amp_update_mode = amp_update_mode
cfg.dyn_type = dyn_type
cfg.prop_type = prop_type
cfg.fid_type = fid_type
cfg.pulse_type = init_pulse_type
cfg.phase_option = phase_option
cfg.amp_lbound = amp_lbound
cfg.amp_ubound = amp_ubound
if log_level == logging.NOTSET:
log_level = logger.getEffectiveLevel()
else:
logger.setLevel(log_level)
cfg.log_level = log_level
# Create the Dynamics instance
if dyn_type == 'GEN_MAT' or dyn_type is None or dyn_type == '':
dyn = dynamics.DynamicsGenMat(cfg)
elif dyn_type == 'UNIT':
dyn = dynamics.DynamicsUnitary(cfg)
elif dyn_type == 'SYMPL':
dyn = dynamics.DynamicsSymplectic(cfg)
else:
raise errors.UsageError("No option for dyn_type: " + dyn_type)
# Create the PropagatorComputer instance
# The default will be typically be the best option
if prop_type == 'DEF' or prop_type is None or prop_type == '':
# Do nothing use the default for the Dynamics
pass
elif prop_type == 'APPROX':
if not isinstance(dyn.prop_computer, propcomp.PropCompApproxGrad):
dyn.prop_computer = propcomp.PropCompApproxGrad(dyn)
elif prop_type == 'DIAG':
if not isinstance(dyn.prop_computer, propcomp.PropCompDiag):
dyn.prop_computer = propcomp.PropCompDiag(dyn)
elif prop_type == 'AUG_MAT':
if not isinstance(dyn.prop_computer, propcomp.PropCompAugMat):
dyn.prop_computer = propcomp.PropCompAugMat(dyn)
elif prop_type == 'FRECHET':
if not isinstance(dyn.prop_computer, propcomp.PropCompFrechet):
dyn.prop_computer = propcomp.PropCompFrechet(dyn)
else:
raise errors.UsageError("No option for prop_type: " + prop_type)
# Create the FideliyComputer instance
# The default will be typically be the best option
# Note: the FidCompTraceDiffApprox is a subclass of FidCompTraceDiff
# so need to check this type first
if fid_type == 'DEF' or fid_type is None or fid_type == '':
# None given, use the default for the Dynamics
pass
elif fid_type == 'TDAPPROX':
if not isinstance(dyn.fid_computer, fidcomp.FidCompTraceDiffApprox):
dyn.fid_computer = fidcomp.FidCompTraceDiffApprox(dyn)
elif fid_type == 'TRACEDIFF':
if not isinstance(dyn.fid_computer, fidcomp.FidCompTraceDiff):
dyn.fid_computer = fidcomp.FidCompTraceDiff(dyn)
elif fid_type == 'UNIT':
if not isinstance(dyn.fid_computer, fidcomp.FidCompUnitary):
dyn.fid_computer = fidcomp.FidCompUnitary(dyn)
else:
raise errors.UsageError("No option for fid_type: " + fid_type)
if isinstance(dyn.fid_computer, fidcomp.FidCompUnitary):
dyn.fid_computer.set_phase_option(phase_option)
if isinstance(dyn.fid_computer, fidcomp.FidCompTraceDiff):
dyn.fid_computer.scale_factor = fid_err_scale_factor
# Create the Optimiser instance
# The class of the object will determine which multivar optimisation
# algorithm is used
if optim_alg == 'BFGS':
optim = optimizer.OptimizerBFGS(cfg, dyn)
elif optim_alg == 'LBFGSB':
optim = optimizer.OptimizerLBFGSB(cfg, dyn)
elif optim_alg is None:
raise errors.UsageError("Optimisation algorithm must be specified "
"via 'optim_alg' parameter")
else:
raise errors.UsageError("No option for optim_alg: " + optim_alg)
# Create the TerminationConditions instance
tc = termcond.TerminationConditions()
tc.fid_err_targ = fid_err_targ
tc.min_gradient_norm = min_grad
tc.max_iterations = max_iter
tc.max_wall_time = max_wall_time
optim.termination_conditions = tc
if gen_stats:
# Create a stats object
# Note that stats object is optional
# if the Dynamics and Optimizer stats attribute is not set
# then no stats will be collected, which could improve performance
if amp_update_mode == 'DYNAMIC':
sts = stats.StatsDynTsUpdate()
else:
sts = stats.Stats()
dyn.stats = sts
optim.stats = sts
# Configure the dynamics
dyn.drift_dyn_gen = drift
dyn.ctrl_dyn_gen = ctrls
dyn.initial = initial
dyn.target = target
if tau is None:
# Check that parameters have been supplied to generate the
# timeslot durations
try:
evo_time / num_tslots
except:
raise errors.UsageError(
"Either the timeslot durations should be supplied as an "
"array 'tau' or the number of timeslots 'num_tslots' "
"and the evolution time 'evo_time' must be given.")
dyn.num_tslots = num_tslots
dyn.evo_time = evo_time
else:
dyn.tau = tau
# this function is called, so that the num_ctrls attribute will be set
dyn.get_num_ctrls()
# Create a pulse generator of the type specified
p_gen = pulsegen.create_pulse_gen(pulse_type=init_pulse_type, dyn=dyn)
p_gen.scaling = pulse_scaling
p_gen.offset = pulse_offset
p_gen.lbound = amp_lbound
p_gen.ubound = amp_ubound
# If the pulse is a periodic type, then set the pulse to be one complete
# wave
if isinstance(p_gen, pulsegen.PulseGenPeriodic):
p_gen.num_waves = 1.0
optim.pulse_generator = p_gen
if log_level <= logging.DEBUG:
logger.debug("Optimisation config summary...\n"
" object classes:\n"
" optimizer: " + optim.__class__.__name__ +
"\n dynamics: " + dyn.__class__.__name__ +
"\n tslotcomp: " +
dyn.tslot_computer.__class__.__name__ +
"\n fidcomp: " + dyn.fid_computer.__class__.__name__ +
"\n propcomp: " +
dyn.prop_computer.__class__.__name__ +
"\n pulsegen: " + p_gen.__class__.__name__)
return optim
tc.fid_err_targ = 1e-3
tc.min_gradient_norm = 1e-10
tc.max_iter_total = 200
tc.max_wall_time_total = 30
tc.break_on_targ = True
optim = optimizer.OptimizerCrabFmin(cfg, dyn)
sts = stats.Stats()
dyn.stats = sts
optim.stats = sts
optim.config = cfg
optim.dynamics = dyn
optim.termination_conditions = tc
guess_pgen = pulsegen.create_pulse_gen('LIN', dyn)
init_amps = np.zeros([n_ts, n_ctrls])
optim.pulse_generator = []
for j in range(n_ctrls):
# Note that for CRAB each control must have its own pulse generator
# as the frequencies and coeffs values are stored in the pulsegen
pgen = pulsegen.PulseGenCrabFourier(dyn)
pgen.scaling = 0.1
# comment out the next line for no guess pulse
pgen.guess_pulse = guess_pgen.gen_pulse()
optim.pulse_generator.append(pgen)
init_amps[:, j] = pgen.gen_pulse()
dyn.initialize_controls(init_amps)
# *************************************************************
# File extension for output files
# Physical parameters
dyn.coupling1 = 0.3
dyn.coupling2 = 0.2
dyn.sqz = 0.5
dyn.rot = 1.0
# load the dynamics parameters
# note these will overide those above if present in the file
print("Loading dynamics parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, dynamics=dyn)
dyn.init_timeslots()
n_ts = dyn.num_tslots
# Create a pulse generator of the type specified
p_gen = pulsegen.create_pulse_gen(pulse_type=cfg.pulse_type, dyn=dyn)
p_gen.lbound = cfg.amp_lbound
p_gen.ubound = cfg.amp_ubound
# load the pulse generator parameters
# note these will overide those above if present in the file
print("Loading pulsegen parameters from {}".format(cfg.param_fpath))
loadparams.load_parameters(cfg.param_fpath, pulsegen=p_gen)
# Create the TerminationConditions instance
tc = termcond.TerminationConditions()
tc.fid_err_targ = 1e-3
tc.min_gradient_norm = 1e-10
tc.max_iter_total = 200
tc.max_wall_time_total = 30
tc.break_on_targ = True
def create_pulse_optimizer(
drift, ctrls, initial, target,
num_tslots=None, evo_time=None, tau=None,
amp_lbound=-np.Inf, amp_ubound=np.Inf,
fid_err_targ=1e-10, min_grad=1e-10,
max_iter=500, max_wall_time=180,
optim_alg='LBFGSB', max_metric_corr=10, accuracy_factor=1e7,
dyn_type='GEN_MAT', prop_type='DEF',
fid_type='DEF', phase_option=None, fid_err_scale_factor=None,
amp_update_mode='ALL',
init_pulse_type='RND', pulse_scaling=1.0, pulse_offset=0.0,
log_level=logging.NOTSET, gen_stats=False):
"""
Generate the objects of the appropriate subclasses
required for the pulse optmisation based on the parameters given
Note this method may be preferable to calling optimize_pulse
if more detailed configuration is required before running the
optmisation algorthim, or the algorithm will be run many times,
for instances when trying to finding global the optimum or
minimum time optimisation
Parameters
----------
drift : Qobj
the underlying dynamics generator of the system
ctrls : List of Qobj
a list of control dynamics generators. These are scaled by
the amplitudes to alter the overall dynamics
initial : Qobj
starting point for the evolution.
Typically the identity matrix
target : Qobj
target transformation, e.g. gate or state, for the time evolution
num_tslots : integer or None
number of timeslots.
None implies that timeslots will be given in the tau array
evo_time : float or None
total time for the evolution
None implies that timeslots will be given in the tau array
tau : array[num_tslots] of floats or None
durations for the timeslots.
if this is given then num_tslots and evo_time are dervived
from it
None implies that timeslot durations will be equal and
calculated as evo_time/num_tslots
amp_lbound : float or list of floats
lower boundaries for the control amplitudes
Can be a scalar value applied to all controls
or a list of bounds for each control
amp_ubound : float or list of floats
upper boundaries for the control amplitudes
Can be a scalar value applied to all controls
or a list of bounds for each control
fid_err_targ : float
Fidelity error target. Pulse optimisation will
terminate when the fidelity error falls below this value
mim_grad : float
Minimum gradient. When the sum of the squares of the
gradients wrt to the control amplitudes falls below this
value, the optimisation terminates, assuming local minima
max_iter : integer
Maximum number of iterations of the optimisation algorithm
max_wall_time : float
Maximum allowed elapsed time for the optimisation algorithm
optim_alg : string
Multi-variable optimisation algorithm
options are BFGS, LBFGSB
(see Optimizer classes for details)
max_metric_corr : integer
The maximum number of variable metric corrections used to define
the limited memory matrix. That is the number of previous
gradient values that are used to approximate the Hessian
see the scipy.optimize.fmin_l_bfgs_b documentation for description
of m argument
(used only in L-BFGS-B)
accuracy_factor : float
Determines the accuracy of the result.
Typical values for accuracy_factor are: 1e12 for low accuracy;
1e7 for moderate accuracy; 10.0 for extremely high accuracy
scipy.optimize.fmin_l_bfgs_b factr argument.
(used only in L-BFGS-B)
dyn_type : string
Dynamics type, i.e. the type of matrix used to describe
the dynamics. Options are UNIT, GEN_MAT, SYMPL
(see Dynamics classes for details)
prop_type : string
Propagator type i.e. the method used to calculate the
propagtors and propagtor gradient for each timeslot
options are DEF, APPROX, DIAG, FRECHET, AUG_MAT
DEF will use the default for the specific dyn_type
(see PropagatorComputer classes for details)
fid_type : string
Fidelity error (and fidelity error gradient) computation method
Options are DEF, UNIT, TRACEDIFF, TD_APPROX
DEF will use the default for the specific dyn_type
(See FideliyComputer classes for details)
phase_option : string
determines how global phase is treated in fidelity
calculations (fid_type='UNIT' only). Options:
PSU - global phase ignored
SU - global phase included
fid_err_scale_factor : float
(used in TRACEDIFF FidelityComputer and subclasses only)
The fidelity error calculated is of some arbitary scale. This
factor can be used to scale the fidelity error such that it may
represent some physical measure
If None is given then it is caculated as 1/2N, where N
is the dimension of the drift.
amp_update_mode : string
determines whether propagators are calculated
Options: DEF, ALL, DYNAMIC (needs work)
DEF will use the default for the specific dyn_type
(See TimeslotComputer classes for details)
init_pulse_type : string
type / shape of pulse(s) used to initialise the
the control amplitudes. Options include:
RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW
(see PulseGen classes for details)
pulse_scaling : float
Linear scale factor for generated pulses
By default initial pulses are generated with amplitudes in the
range (-1.0, 1.0). These will be scaled by this parameter
pulse_offset : float
Line offset for the pulse. That is this value will be added
to any initial pulses generated.
log_level : integer
level of messaging output from the logger.
Options are attributes of qutip.logging,
in decreasing levels of messaging, are:
DEBUG_INTENSE, DEBUG_VERBOSE, DEBUG, INFO, WARN, ERROR, CRITICAL
Anything WARN or above is effectively 'quiet' execution,
assuming everything runs as expected.
The default NOTSET implies that the level will be taken from
the QuTiP settings file, which by default is WARN
Note value should be set using set_log_level
gen_stats : boolean
if set to True then statistics for the optimisation
run will be generated - accessible through attributes
of the stats object
Returns
-------
Instance of an Optimizer, through which the
Config, Dynamics, PulseGen, and TerminationConditions objects
can be accessed as attributes.
The PropagatorComputer, FidelityComputer and TimeslotComputer objects
can be accessed as attributes of the Dynamics object, e.g.
optimizer.dynamics.fid_computer
The optimisation can be run through the optimizer.run_optimization
"""
# check parameters
if not isinstance(drift, Qobj):
raise TypeError("drift must be a Qobj")
else:
drift = drift.full()
if not isinstance(ctrls, (list, tuple)):
raise TypeError("ctrls should be a list of Qobj")
else:
j = 0
for ctrl in ctrls:
if not isinstance(ctrl, Qobj):
raise TypeError("ctrls should be a list of Qobj")
else:
ctrls[j] = ctrl.full()
j += 1
if not isinstance(initial, Qobj):
raise TypeError("initial must be a Qobj")
else:
initial = initial.full()
if not isinstance(target, Qobj):
raise TypeError("target must be a Qobj")
else:
target = target.full()
cfg = optimconfig.OptimConfig()
cfg.optim_alg = optim_alg
cfg.max_metric_corr = max_metric_corr
cfg.accuracy_factor = accuracy_factor
cfg.amp_update_mode = amp_update_mode
cfg.dyn_type = dyn_type
cfg.prop_type = prop_type
cfg.fid_type = fid_type
cfg.pulse_type = init_pulse_type
cfg.phase_option = phase_option
cfg.amp_lbound = amp_lbound
cfg.amp_ubound = amp_ubound
if log_level == logging.NOTSET:
log_level = logger.getEffectiveLevel()
else:
logger.setLevel(log_level)
cfg.log_level = log_level
# Create the Dynamics instance
if dyn_type == 'GEN_MAT' or dyn_type is None or dyn_type == '':
dyn = dynamics.DynamicsGenMat(cfg)
elif dyn_type == 'UNIT':
dyn = dynamics.DynamicsUnitary(cfg)
elif dyn_type == 'SYMPL':
dyn = dynamics.DynamicsSymplectic(cfg)
else:
raise errors.UsageError("No option for dyn_type: " + dyn_type)
# Create the PropagatorComputer instance
# The default will be typically be the best option
if prop_type == 'DEF' or prop_type is None or prop_type == '':
# Do nothing use the default for the Dynamics
pass
elif prop_type == 'APPROX':
if not isinstance(dyn.prop_computer, propcomp.PropCompApproxGrad):
dyn.prop_computer = propcomp.PropCompApproxGrad(dyn)
elif prop_type == 'DIAG':
if not isinstance(dyn.prop_computer, propcomp.PropCompDiag):
dyn.prop_computer = propcomp.PropCompDiag(dyn)
elif prop_type == 'AUG_MAT':
if not isinstance(dyn.prop_computer, propcomp.PropCompAugMat):
dyn.prop_computer = propcomp.PropCompAugMat(dyn)
elif prop_type == 'FRECHET':
if not isinstance(dyn.prop_computer, propcomp.PropCompFrechet):
dyn.prop_computer = propcomp.PropCompFrechet(dyn)
else:
raise errors.UsageError("No option for prop_type: " + prop_type)
# Create the FideliyComputer instance
# The default will be typically be the best option
# Note: the FidCompTraceDiffApprox is a subclass of FidCompTraceDiff
# so need to check this type first
if fid_type == 'DEF' or fid_type is None or fid_type == '':
# None given, use the default for the Dynamics
pass
elif fid_type == 'TDAPPROX':
if not isinstance(dyn.fid_computer, fidcomp.FidCompTraceDiffApprox):
dyn.fid_computer = fidcomp.FidCompTraceDiffApprox(dyn)
elif fid_type == 'TRACEDIFF':
if not isinstance(dyn.fid_computer, fidcomp.FidCompTraceDiff):
dyn.fid_computer = fidcomp.FidCompTraceDiff(dyn)
elif fid_type == 'UNIT':
if not isinstance(dyn.fid_computer, fidcomp.FidCompUnitary):
dyn.fid_computer = fidcomp.FidCompUnitary(dyn)
else:
raise errors.UsageError("No option for fid_type: " + fid_type)
if isinstance(dyn.fid_computer, fidcomp.FidCompUnitary):
dyn.fid_computer.set_phase_option(phase_option)
if isinstance(dyn.fid_computer, fidcomp.FidCompTraceDiff):
dyn.fid_computer.scale_factor = fid_err_scale_factor
# Create the Optimiser instance
# The class of the object will determine which multivar optimisation
# algorithm is used
if optim_alg == 'BFGS':
optim = optimizer.OptimizerBFGS(cfg, dyn)
elif optim_alg == 'LBFGSB':
optim = optimizer.OptimizerLBFGSB(cfg, dyn)
elif optim_alg is None:
raise errors.UsageError("Optimisation algorithm must be specified "
"via 'optim_alg' parameter")
else:
raise errors.UsageError("No option for optim_alg: " + optim_alg)
# Create the TerminationConditions instance
tc = termcond.TerminationConditions()
tc.fid_err_targ = fid_err_targ
tc.min_gradient_norm = min_grad
tc.max_iterations = max_iter
tc.max_wall_time = max_wall_time
optim.termination_conditions = tc
if gen_stats:
# Create a stats object
# Note that stats object is optional
# if the Dynamics and Optimizer stats attribute is not set
# then no stats will be collected, which could improve performance
if amp_update_mode == 'DYNAMIC':
sts = stats.StatsDynTsUpdate()
else:
sts = stats.Stats()
dyn.stats = sts
optim.stats = sts
# Configure the dynamics
dyn.drift_dyn_gen = drift
dyn.ctrl_dyn_gen = ctrls
dyn.initial = initial
dyn.target = target
if tau is None:
# Check that parameters have been supplied to generate the
# timeslot durations
try:
evo_time / num_tslots
except:
raise errors.UsageError(
"Either the timeslot durations should be supplied as an "
"array 'tau' or the number of timeslots 'num_tslots' "
"and the evolution time 'evo_time' must be given.")
dyn.num_tslots = num_tslots
dyn.evo_time = evo_time
else:
dyn.tau = tau
# this function is called, so that the num_ctrls attribute will be set
dyn.get_num_ctrls()
# Create a pulse generator of the type specified
p_gen = pulsegen.create_pulse_gen(pulse_type=init_pulse_type, dyn=dyn)
p_gen.scaling = pulse_scaling
p_gen.offset = pulse_offset
p_gen.lbound = amp_lbound
p_gen.ubound = amp_ubound
# If the pulse is a periodic type, then set the pulse to be one complete
# wave
if isinstance(p_gen, pulsegen.PulseGenPeriodic):
p_gen.num_waves = 1.0
optim.pulse_generator = p_gen
if log_level <= logging.DEBUG:
logger.debug(
"Optimisation config summary...\n"
" object classes:\n"
" optimizer: " + optim.__class__.__name__ +
"\n dynamics: " + dyn.__class__.__name__ +
"\n tslotcomp: " + dyn.tslot_computer.__class__.__name__ +
"\n fidcomp: " + dyn.fid_computer.__class__.__name__ +
"\n propcomp: " + dyn.prop_computer.__class__.__name__ +
"\n pulsegen: " + p_gen.__class__.__name__)
return optim
print("Configuring optimiser objects")
# **** get handles to the other objects ****
optim.test_out_files = 0
dyn = optim.dynamics
dyn.test_out_files = 0
p_gen = optim.pulse_generator
for i in range(n_evo_times):
# Generate the tau (duration) and time (cumulative) arrays
# so that it can be used to create the pulse generator
# with matching timeslots
dyn.init_timeslots()
if i > 0:
# Create a new pulse generator for the new dynamics
p_gen = pulsegen.create_pulse_gen(p_type, dyn)
# Generate different pulses for each control
init_amps = np.zeros([n_ts, n_ctrls])
if p_gen.periodic:
phase_diff = np.pi / n_ctrls
for j in range(n_ctrls):
init_amps[:, j] = p_gen.gen_pulse(start_phase=phase_diff * j)
elif isinstance(p_gen, pulsegen.PulseGenLinear):
for j in range(n_ctrls):
p_gen.scaling = float(j) - float(n_ctrls - 1) / 2
init_amps[:, j] = p_gen.gen_pulse()
elif isinstance(p_gen, pulsegen.PulseGenZero):
for j in range(n_ctrls):
p_gen.offset = sf = float(j) - float(n_ctrls - 1) / 2
init_amps[:, j] = p_gen.gen_pulse()
gen_stats=True)
# **** get handles to the other objects ****
optim.test_out_files = 0
dyn = optim.dynamics
dyn.test_out_files = 0
p_gen = optim.pulse_generator
for i in range(n_evo_times):
# Generate the tau (duration) and time (cumulative) arrays
# so that it can be used to create the pulse generator
# with matching timeslots
dyn.init_timeslots()
if i > 0:
# Create a new pulse generator for the new dynamics
p_gen = pulsegen.create_pulse_gen(p_type, dyn)
#Generate different initial pulses for each of the controls
init_amps = np.zeros([n_ts, n_ctrls])
if (p_gen.periodic):
phase_diff = np.pi / n_ctrls
for j in range(n_ctrls):
init_amps[:, j] = p_gen.gen_pulse(start_phase=phase_diff * j)
elif (isinstance(p_gen, pulsegen.PulseGenLinear)):
for j in range(n_ctrls):
p_gen.scaling = float(j) - float(n_ctrls - 1) / 2
init_amps[:, j] = p_gen.gen_pulse()
elif (isinstance(p_gen, pulsegen.PulseGenZero)):
for j in range(n_ctrls):
p_gen.offset = sf = float(j) - float(n_ctrls - 1) / 2
init_amps[:, j] = p_gen.gen_pulse()
def run_optimizer(optim, dt, n_evo_times, dyn, p_type, n_ts, n_ctrls, f_ext,
evo_time, evo_times, p_gen):
results = []
for i in range(n_evo_times):
# Generate the tau (duration) and time (cumulative) arrays
# so that it can be used to create the pulse generator
# with matching timeslots
dyn.init_timeslots()
if i > 0:
# Create a new pulse generator for the new dynamics
p_gen = pulsegen.create_pulse_gen(p_type, dyn)
# Generate different initial pulses for each of the controls
init_amps = np.zeros([n_ts, n_ctrls])
if (p_gen.periodic):
phase_diff = np.pi / n_ctrls
for j in range(n_ctrls):
init_amps[:, j] = p_gen.gen_pulse(start_phase=phase_diff * j)
# if alg == 'CRAB':
# for j in range(n_ctrls)
elif (isinstance(p_gen, pulsegen.PulseGenLinear)):
for j in range(n_ctrls):
p_gen.scaling = float(j) - float(n_ctrls - 1) / 2
init_amps[:, j] = p_gen.gen_pulse()
elif (isinstance(p_gen, pulsegen.PulseGenZero)):
for j in range(n_ctrls):
p_gen.offset = sf = float(j) - float(n_ctrls - 1) / 2
init_amps[:, j] = p_gen.gen_pulse()
else:
# Should be random pulse
for j in range(n_ctrls):
init_amps[:, j] = p_gen.gen_pulse()
dyn.initialize_controls(init_amps)
# Save initial amplitudes to a text file
if f_ext is not None:
pulsefile = "ctrl_amps_initial_" + f_ext
dyn.save_amps(pulsefile)
print("Initial amplitudes output to file: " + pulsefile)
print("***********************************")
print("\n+++++++++++++++++++++++++++++++++++")
print("Starting pulse optimisation for T={}".format(evo_time))
print("+++++++++++++++++++++++++++++++++++\n")
result = optim.run_optimization()
# Save final amplitudes to a text file
if f_ext is not None:
pulsefile = "ctrl_amps_final_" + f_ext
dyn.save_amps(pulsefile)
print("Final amplitudes output to file: " + pulsefile)
# Report the results
result.stats.report()
print("Final evolution\n{}\n".format(result.evo_full_final))
print("********* Summary *****************")
print("Final fidelity error {}".format(result.fid_err))
print("Final gradient normal {}".format(result.grad_norm_final))
print("Terminated due to {}".format(result.termination_reason))
print("Number of iterations {}".format(result.num_iter))
print("Completed in {} HH:MM:SS.US".format(
datetime.timedelta(seconds=result.wall_time)))
results.append(result)
if i + 1 < len(evo_times):
# reconfigure the dynamics for the next evo time
evo_time = evo_times[i + 1]
n_ts = int(float(evo_time) / dt)
dyn.tau = None
dyn.evo_time = evo_time
dyn.num_tslots = n_ts
return results
