def _create_computers(self):
"""
Create the default timeslot, fidelity and propagator computers
"""
# The time slot computer. By default it is set to _UpdateAll
# can be set to _DynUpdate in the configuration
# (see class file for details)
if self.config.amp_update_mode == 'DYNAMIC':
self.tslot_computer = tslotcomp.TSlotCompDynUpdate(self)
else:
self.tslot_computer = tslotcomp.TSlotCompUpdateAll(self)
self.prop_computer = propcomp.PropCompFrechet(self)
self.fid_computer = fidcomp.FidCompTraceDiff(self)
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