# -*- coding: utf-8 -*-
# This file is part of QuTiP: Quantum Toolbox in Python.
#
# Copyright (c) 2014 and later, Alexander J G Pitchford
# All rights reserved.
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# @author: Alexander Pitchford
# @email1: agp1@aber.ac.uk
# @email2: alex.pitchford@gmail.com
# @organization: Aberystwyth University
# @supervisor: Daniel Burgarth
"""
Wrapper functions that will manage the creation of the objects,
build the configuration, and execute the algorithm required to optimise
a set of ctrl pulses for a given (quantum) system.
The fidelity error is some measure of distance of the system evolution
from the given target evolution in the time allowed for the evolution.
The functions minimise this fidelity error wrt the piecewise control
amplitudes in the timeslots
There are currently two quantum control pulse optmisations algorithms
implemented in this library. There are accessible through the methods
in this module. Both the algorithms use the scipy.optimize methods
to minimise the fidelity error with respect to to variables that define
the pulse.
GRAPE
-----
The default algorithm (as it was implemented here first) is GRAPE
GRadient Ascent Pulse Engineering [1][2]. It uses a gradient based method such
as BFGS to minimise the fidelity error. This makes convergence very quick
when an exact gradient can be calculated, but this limits the factors that can
taken into account in the fidelity.
CRAB
----
The CRAB [3][4] algorithm was developed at the University of Ulm.
In full it is the Chopped RAndom Basis algorithm.
The main difference is that it reduces the number of optimisation variables
by defining the control pulses by expansions of basis functions,
where the variables are the coefficients. Typically a Fourier series is chosen,
i.e. the variables are the Fourier coefficients.
Therefore it does not need to compute an explicit gradient.
By default it uses the Nelder-Mead method for fidelity error minimisation.
References
----------
1. N Khaneja et. al.
Optimal control of coupled spin dynamics: Design of NMR pulse sequences
by gradient ascent algorithms. J. Magn. Reson. 172, 296–305 (2005).
2. Shai Machnes et.al
DYNAMO - Dynamic Framework for Quantum Optimal Control
arXiv.1011.4874
3. Doria, P., Calarco, T. & Montangero, S.
Optimal Control Technique for Many-Body Quantum Dynamics.
Phys. Rev. Lett. 106, 1–4 (2011).
4. Caneva, T., Calarco, T. & Montangero, S.
Chopped random-basis quantum optimization.
Phys. Rev. A - At. Mol. Opt. Phys. 84, (2011).
"""
import numpy as np
import warnings
# QuTiP
from qutip.qobj import Qobj
import qutip.logging_utils as logging
logger = logging.get_logger()
# QuTiP control modules
import qutip.control.optimconfig as optimconfig
import qutip.control.dynamics as dynamics
import qutip.control.termcond as termcond
import qutip.control.optimizer as optimizer
import qutip.control.stats as stats
import qutip.control.errors as errors
import qutip.control.fidcomp as fidcomp
import qutip.control.propcomp as propcomp
import qutip.control.pulsegen as pulsegen
#import qutip.control.pulsegencrab as pulsegencrab
warnings.simplefilter('always', DeprecationWarning) #turn off filter
def _param_deprecation(message, stacklevel=3):
"""
Issue deprecation warning
Using stacklevel=3 will ensure message refers the function
calling with the deprecated parameter,
"""
warnings.warn(message, DeprecationWarning, stacklevel=stacklevel)
def _upper_safe(s):
try:
s = s.upper()
except:
pass
return s
[docs]def optimize_pulse(
drift, ctrls, initial, target,
num_tslots=None, evo_time=None, tau=None,
amp_lbound=None, amp_ubound=None,
fid_err_targ=1e-10, min_grad=1e-10,
max_iter=500, max_wall_time=180,
alg='GRAPE', alg_params=None,
optim_params=None, optim_method='DEF', method_params=None,
optim_alg=None, max_metric_corr=None, accuracy_factor=None,
dyn_type='GEN_MAT', dyn_params=None,
prop_type='DEF', prop_params=None,
fid_type='DEF', fid_params=None,
phase_option=None, fid_err_scale_factor=None,
tslot_type='DEF', tslot_params=None,
amp_update_mode=None,
init_pulse_type='DEF', init_pulse_params=None,
pulse_scaling=1.0, pulse_offset=0.0,
ramping_pulse_type=None, ramping_pulse_params=None,
log_level=logging.NOTSET, out_file_ext=None, gen_stats=False):
"""
Optimise a control pulse to minimise the fidelity error.
The dynamics of the system in any given timeslot are governed
by the combined dynamics generator,
i.e. the sum of the drift+ctrl_amp[j]*ctrls[j]
The control pulse is an [n_ts, n_ctrls)] array of piecewise amplitudes
Starting from an intital (typically random) pulse,
a multivariable optimisation algorithm attempts to determines the
optimal values for the control pulse to minimise the fidelity error
The fidelity error is some measure of distance of the system evolution
from the given target evolution in the time allowed for the evolution.
Parameters
----------
drift : Qobj or list of Qobj
the underlying dynamics generator of the system
can provide list (of length num_tslots) for time dependent drift
ctrls : List of Qobj or array like [num_tslots, evo_time]
a list of control dynamics generators. These are scaled by
the amplitudes to alter the overall dynamics
Array like imput can be provided for time dependent control generators
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
alg : string
Algorithm to use in pulse optimisation.
Options are:
'GRAPE' (default) - GRadient Ascent Pulse Engineering
'CRAB' - Chopped RAndom Basis
alg_params : Dictionary
options that are specific to the algorithm see above
optim_params : Dictionary
The key value pairs are the attribute name and value
used to set attribute values
Note: attributes are created if they do not exist already,
and are overwritten if they do.
Note: method_params are applied afterwards and so may override these
optim_method : string
a scipy.optimize.minimize method that will be used to optimise
the pulse for minimum fidelity error
Note that FMIN, FMIN_BFGS & FMIN_L_BFGS_B will all result
in calling these specific scipy.optimize methods
Note the LBFGSB is equivalent to FMIN_L_BFGS_B for backwards
capatibility reasons.
Supplying DEF will given alg dependent result:
GRAPE - Default optim_method is FMIN_L_BFGS_B
CRAB - Default optim_method is FMIN
method_params : dict
Parameters for the optim_method.
Note that where there is an attribute of the
Optimizer object or the termination_conditions matching the key
that attribute. Otherwise, and in some case also,
they are assumed to be method_options
for the scipy.optimize.minimize method.
optim_alg : string
Deprecated. Use optim_method.
max_metric_corr : integer
Deprecated. Use method_params instead
accuracy_factor : float
Deprecated. Use method_params instead
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)
dyn_params : dict
Parameters for the Dynamics object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
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)
prop_params : dict
Parameters for the PropagatorComputer object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
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 FidelityComputer classes for details)
fid_params : dict
Parameters for the FidelityComputer object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
phase_option : string
Deprecated. Pass in fid_params instead.
fid_err_scale_factor : float
Deprecated. Use scale_factor key in fid_params instead.
tslot_type : string
Method for computing the dynamics generators, propagators and
evolution in the timeslots.
Options: DEF, UPDATE_ALL, DYNAMIC
UPDATE_ALL is the only one that currently works
(See TimeslotComputer classes for details)
tslot_params : dict
Parameters for the TimeslotComputer object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
amp_update_mode : string
Deprecated. Use tslot_type instead.
init_pulse_type : string
type / shape of pulse(s) used to initialise the
the control amplitudes.
Options (GRAPE) include:
RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW
DEF is RND
(see PulseGen classes for details)
For the CRAB the this the guess_pulse_type.
init_pulse_params : dict
Parameters for the initial / guess pulse generator object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
pulse_scaling : float
Linear scale factor for generated initial / guess 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
Linear offset for the pulse. That is this value will be added
to any initial / guess pulses generated.
ramping_pulse_type : string
Type of pulse used to modulate the control pulse.
It's intended use for a ramping modulation, which is often required in
experimental setups.
This is only currently implemented in CRAB.
GAUSSIAN_EDGE was added for this purpose.
ramping_pulse_params : dict
Parameters for the ramping pulse generator object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
log_level : integer
level of messaging output from the logger.
Options are attributes of qutip.logging_utils,
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
out_file_ext : string or None
files containing the initial and final control pulse
amplitudes are saved to the current directory.
The default name will be postfixed with this extension
Setting this to None will suppress the output of files
gen_stats : boolean
if set to True then statistics for the optimisation
run will be generated - accessible through attributes
of the stats object
Returns
-------
opt : OptimResult
Returns instance of OptimResult, which has attributes giving the
reason for termination, final fidelity error, final evolution
final amplitudes, statistics etc
"""
if log_level == logging.NOTSET:
log_level = logger.getEffectiveLevel()
else:
logger.setLevel(log_level)
# The parameters types are checked in create_pulse_optimizer
# so no need to do so here
# However, the deprecation management is repeated here
# so that the stack level is correct
if not optim_alg is None:
optim_method = optim_alg
_param_deprecation(
"The 'optim_alg' parameter is deprecated. "
"Use 'optim_method' instead")
if not max_metric_corr is None:
if isinstance(method_params, dict):
if not 'max_metric_corr' in method_params:
method_params['max_metric_corr'] = max_metric_corr
else:
method_params = {'max_metric_corr':max_metric_corr}
_param_deprecation(
"The 'max_metric_corr' parameter is deprecated. "
"Use 'max_metric_corr' in method_params instead")
if not accuracy_factor is None:
if isinstance(method_params, dict):
if not 'accuracy_factor' in method_params:
method_params['accuracy_factor'] = accuracy_factor
else:
method_params = {'accuracy_factor':accuracy_factor}
_param_deprecation(
"The 'accuracy_factor' parameter is deprecated. "
"Use 'accuracy_factor' in method_params instead")
# phase_option
if not phase_option is None:
if isinstance(fid_params, dict):
if not 'phase_option' in fid_params:
fid_params['phase_option'] = phase_option
else:
fid_params = {'phase_option':phase_option}
_param_deprecation(
"The 'phase_option' parameter is deprecated. "
"Use 'phase_option' in fid_params instead")
# fid_err_scale_factor
if not fid_err_scale_factor is None:
if isinstance(fid_params, dict):
if not 'fid_err_scale_factor' in fid_params:
fid_params['scale_factor'] = fid_err_scale_factor
else:
fid_params = {'scale_factor':fid_err_scale_factor}
_param_deprecation(
"The 'fid_err_scale_factor' parameter is deprecated. "
"Use 'scale_factor' in fid_params instead")
# amp_update_mode
if not amp_update_mode is None:
amp_update_mode_up = _upper_safe(amp_update_mode)
if amp_update_mode_up == 'ALL':
tslot_type = 'UPDATE_ALL'
else:
tslot_type = amp_update_mode
_param_deprecation(
"The 'amp_update_mode' parameter is deprecated. "
"Use 'tslot_type' instead")
optim = create_pulse_optimizer(
drift, ctrls, initial, target,
num_tslots=num_tslots, evo_time=evo_time, tau=tau,
amp_lbound=amp_lbound, amp_ubound=amp_ubound,
fid_err_targ=fid_err_targ, min_grad=min_grad,
max_iter=max_iter, max_wall_time=max_wall_time,
alg=alg, alg_params=alg_params, optim_params=optim_params,
optim_method=optim_method, method_params=method_params,
dyn_type=dyn_type, dyn_params=dyn_params,
prop_type=prop_type, prop_params=prop_params,
fid_type=fid_type, fid_params=fid_params,
init_pulse_type=init_pulse_type, init_pulse_params=init_pulse_params,
pulse_scaling=pulse_scaling, pulse_offset=pulse_offset,
ramping_pulse_type=ramping_pulse_type,
ramping_pulse_params=ramping_pulse_params,
log_level=log_level, gen_stats=gen_stats)
dyn = optim.dynamics
dyn.init_timeslots()
# Generate initial pulses for each control
init_amps = np.zeros([dyn.num_tslots, dyn.num_ctrls])
if alg == 'CRAB':
for j in range(dyn.num_ctrls):
pgen = optim.pulse_generator[j]
pgen.init_pulse()
init_amps[:, j] = pgen.gen_pulse()
else:
pgen = optim.pulse_generator
for j in range(dyn.num_ctrls):
init_amps[:, j] = pgen.gen_pulse()
# Initialise the starting amplitudes
dyn.initialize_controls(init_amps)
if log_level <= logging.INFO:
msg = "System configuration:\n"
dg_name = "dynamics generator"
if dyn_type == 'UNIT':
dg_name = "Hamiltonian"
if dyn.time_depend_drift:
msg += "Initial drift {}:\n".format(dg_name)
msg += str(dyn.drift_dyn_gen[0])
else:
msg += "Drift {}:\n".format(dg_name)
msg += str(dyn.drift_dyn_gen)
for j in range(dyn.num_ctrls):
msg += "\nControl {} {}:\n".format(j+1, dg_name)
msg += str(dyn.ctrl_dyn_gen[j])
msg += "\nInitial state / operator:\n"
msg += str(dyn.initial)
msg += "\nTarget state / operator:\n"
msg += str(dyn.target)
logger.info(msg)
if out_file_ext is not None:
# Save initial amplitudes to a text file
pulsefile = "ctrl_amps_initial_" + out_file_ext
dyn.save_amps(pulsefile)
if log_level <= logging.INFO:
logger.info("Initial amplitudes output to file: " + pulsefile)
# Start the optimisation
result = optim.run_optimization()
if out_file_ext is not None:
# Save final amplitudes to a text file
pulsefile = "ctrl_amps_final_" + out_file_ext
dyn.save_amps(pulsefile)
if log_level <= logging.INFO:
logger.info("Final amplitudes output to file: " + pulsefile)
return result
[docs]def optimize_pulse_unitary(
H_d, H_c, U_0, U_targ,
num_tslots=None, evo_time=None, tau=None,
amp_lbound=None, amp_ubound=None,
fid_err_targ=1e-10, min_grad=1e-10,
max_iter=500, max_wall_time=180,
alg='GRAPE', alg_params=None,
optim_params=None, optim_method='DEF', method_params=None,
optim_alg=None, max_metric_corr=None, accuracy_factor=None,
phase_option='PSU',
dyn_params=None, prop_params=None, fid_params=None,
tslot_type='DEF', tslot_params=None,
amp_update_mode=None,
init_pulse_type='DEF', init_pulse_params=None,
pulse_scaling=1.0, pulse_offset=0.0,
ramping_pulse_type=None, ramping_pulse_params=None,
log_level=logging.NOTSET, out_file_ext=None, gen_stats=False):
"""
Optimise a control pulse to minimise the fidelity error, assuming that
the dynamics of the system are generated by unitary operators.
This function is simply a wrapper for optimize_pulse, where the
appropriate options for unitary dynamics are chosen and the parameter
names are in the format familiar to unitary dynamics
The dynamics of the system in any given timeslot are governed
by the combined Hamiltonian,
i.e. the sum of the H_d + ctrl_amp[j]*H_c[j]
The control pulse is an [n_ts, n_ctrls] array of piecewise amplitudes
Starting from an intital (typically random) pulse,
a multivariable optimisation algorithm attempts to determines the
optimal values for the control pulse to minimise the fidelity error
The maximum fidelity for a unitary system is 1, i.e. when the
time evolution resulting from the pulse is equivalent to the target.
And therefore the fidelity error is 1 - fidelity
Parameters
----------
H_d : Qobj or list of Qobj
Drift (aka system) the underlying Hamiltonian of the system
can provide list (of length num_tslots) for time dependent drift
H_c : List of Qobj or array like [num_tslots, evo_time]
a list of control Hamiltonians. These are scaled by
the amplitudes to alter the overall dynamics
Array like imput can be provided for time dependent control generators
U_0 : Qobj
starting point for the evolution.
Typically the identity matrix
U_targ : 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
alg : string
Algorithm to use in pulse optimisation.
Options are:
'GRAPE' (default) - GRadient Ascent Pulse Engineering
'CRAB' - Chopped RAndom Basis
alg_params : Dictionary
options that are specific to the algorithm see above
optim_params : Dictionary
The key value pairs are the attribute name and value
used to set attribute values
Note: attributes are created if they do not exist already,
and are overwritten if they do.
Note: method_params are applied afterwards and so may override these
optim_method : string
a scipy.optimize.minimize method that will be used to optimise
the pulse for minimum fidelity error
Note that FMIN, FMIN_BFGS & FMIN_L_BFGS_B will all result
in calling these specific scipy.optimize methods
Note the LBFGSB is equivalent to FMIN_L_BFGS_B for backwards
capatibility reasons.
Supplying DEF will given alg dependent result:
GRAPE - Default optim_method is FMIN_L_BFGS_B
CRAB - Default optim_method is FMIN
method_params : dict
Parameters for the optim_method.
Note that where there is an attribute of the
Optimizer object or the termination_conditions matching the key
that attribute. Otherwise, and in some case also,
they are assumed to be method_options
for the scipy.optimize.minimize method.
optim_alg : string
Deprecated. Use optim_method.
max_metric_corr : integer
Deprecated. Use method_params instead
accuracy_factor : float
Deprecated. Use method_params instead
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
dyn_params : dict
Parameters for the Dynamics object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
prop_params : dict
Parameters for the PropagatorComputer object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
fid_params : dict
Parameters for the FidelityComputer object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
tslot_type : string
Method for computing the dynamics generators, propagators and
evolution in the timeslots.
Options: DEF, UPDATE_ALL, DYNAMIC
UPDATE_ALL is the only one that currently works
(See TimeslotComputer classes for details)
tslot_params : dict
Parameters for the TimeslotComputer object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
amp_update_mode : string
Deprecated. Use tslot_type instead.
init_pulse_type : string
type / shape of pulse(s) used to initialise the
the control amplitudes.
Options (GRAPE) include:
RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW
DEF is RND
(see PulseGen classes for details)
For the CRAB the this the guess_pulse_type.
init_pulse_params : dict
Parameters for the initial / guess pulse generator object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
pulse_scaling : float
Linear scale factor for generated initial / guess 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
Linear offset for the pulse. That is this value will be added
to any initial / guess pulses generated.
ramping_pulse_type : string
Type of pulse used to modulate the control pulse.
It's intended use for a ramping modulation, which is often required in
experimental setups.
This is only currently implemented in CRAB.
GAUSSIAN_EDGE was added for this purpose.
ramping_pulse_params : dict
Parameters for the ramping pulse generator object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
log_level : integer
level of messaging output from the logger.
Options are attributes of qutip.logging_utils,
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
out_file_ext : string or None
files containing the initial and final control pulse
amplitudes are saved to the current directory.
The default name will be postfixed with this extension
Setting this to None will suppress the output of files
gen_stats : boolean
if set to True then statistics for the optimisation
run will be generated - accessible through attributes
of the stats object
Returns
-------
opt : OptimResult
Returns instance of OptimResult, which has attributes giving the
reason for termination, final fidelity error, final evolution
final amplitudes, statistics etc
"""
# parameters are checked in create pulse optimiser
# The deprecation management is repeated here
# so that the stack level is correct
if not optim_alg is None:
optim_method = optim_alg
_param_deprecation(
"The 'optim_alg' parameter is deprecated. "
"Use 'optim_method' instead")
if not max_metric_corr is None:
if isinstance(method_params, dict):
if not 'max_metric_corr' in method_params:
method_params['max_metric_corr'] = max_metric_corr
else:
method_params = {'max_metric_corr':max_metric_corr}
_param_deprecation(
"The 'max_metric_corr' parameter is deprecated. "
"Use 'max_metric_corr' in method_params instead")
if not accuracy_factor is None:
if isinstance(method_params, dict):
if not 'accuracy_factor' in method_params:
method_params['accuracy_factor'] = accuracy_factor
else:
method_params = {'accuracy_factor':accuracy_factor}
_param_deprecation(
"The 'accuracy_factor' parameter is deprecated. "
"Use 'accuracy_factor' in method_params instead")
# amp_update_mode
if not amp_update_mode is None:
amp_update_mode_up = _upper_safe(amp_update_mode)
if amp_update_mode_up == 'ALL':
tslot_type = 'UPDATE_ALL'
else:
tslot_type = amp_update_mode
_param_deprecation(
"The 'amp_update_mode' parameter is deprecated. "
"Use 'tslot_type' instead")
# phase_option is still valid for this method
# pass it via the fid_params
if not phase_option is None:
if fid_params is None:
fid_params = {'phase_option':phase_option}
else:
if not 'phase_option' in fid_params:
fid_params['phase_option'] = phase_option
return optimize_pulse(
drift=H_d, ctrls=H_c, initial=U_0, target=U_targ,
num_tslots=num_tslots, evo_time=evo_time, tau=tau,
amp_lbound=amp_lbound, amp_ubound=amp_ubound,
fid_err_targ=fid_err_targ, min_grad=min_grad,
max_iter=max_iter, max_wall_time=max_wall_time,
alg=alg, alg_params=alg_params, optim_params=optim_params,
optim_method=optim_method, method_params=method_params,
dyn_type='UNIT', dyn_params=dyn_params,
prop_params=prop_params, fid_params=fid_params,
init_pulse_type=init_pulse_type, init_pulse_params=init_pulse_params,
pulse_scaling=pulse_scaling, pulse_offset=pulse_offset,
ramping_pulse_type=ramping_pulse_type,
ramping_pulse_params=ramping_pulse_params,
log_level=log_level, out_file_ext=out_file_ext,
gen_stats=gen_stats)
[docs]def opt_pulse_crab(
drift, ctrls, initial, target,
num_tslots=None, evo_time=None, tau=None,
amp_lbound=None, amp_ubound=None,
fid_err_targ=1e-5,
max_iter=500, max_wall_time=180,
alg_params=None,
num_coeffs=None, init_coeff_scaling=1.0,
optim_params=None, optim_method='fmin', method_params=None,
dyn_type='GEN_MAT', dyn_params=None,
prop_type='DEF', prop_params=None,
fid_type='DEF', fid_params=None,
tslot_type='DEF', tslot_params=None,
guess_pulse_type=None, guess_pulse_params=None,
guess_pulse_scaling=1.0, guess_pulse_offset=0.0,
guess_pulse_action='MODULATE',
ramping_pulse_type=None, ramping_pulse_params=None,
log_level=logging.NOTSET, out_file_ext=None, gen_stats=False):
"""
Optimise a control pulse to minimise the fidelity error.
The dynamics of the system in any given timeslot are governed
by the combined dynamics generator,
i.e. the sum of the drift+ctrl_amp[j]*ctrls[j]
The control pulse is an [n_ts, n_ctrls] array of piecewise amplitudes.
The CRAB algorithm uses basis function coefficents as the variables to
optimise. It does NOT use any gradient function.
A multivariable optimisation algorithm attempts to determines the
optimal values for the control pulse to minimise the fidelity error
The fidelity error is some measure of distance of the system evolution
from the given target evolution in the time allowed for the evolution.
Parameters
----------
drift : Qobj or list of Qobj
the underlying dynamics generator of the system
can provide list (of length num_tslots) for time dependent drift
ctrls : List of Qobj or array like [num_tslots, evo_time]
a list of control dynamics generators. These are scaled by
the amplitudes to alter the overall dynamics
Array like imput can be provided for time dependent control generators
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
max_iter : integer
Maximum number of iterations of the optimisation algorithm
max_wall_time : float
Maximum allowed elapsed time for the optimisation algorithm
alg_params : Dictionary
options that are specific to the algorithm see above
optim_params : Dictionary
The key value pairs are the attribute name and value
used to set attribute values
Note: attributes are created if they do not exist already,
and are overwritten if they do.
Note: method_params are applied afterwards and so may override these
coeff_scaling : float
Linear scale factor for the random basis coefficients
By default these range from -1.0 to 1.0
Note this is overridden by alg_params (if given there)
num_coeffs : integer
Number of coefficients used for each basis function
Note this is calculated automatically based on the dimension of the
dynamics if not given. It is crucial to the performane of the
algorithm that it is set as low as possible, while still giving
high enough frequencies.
Note this is overridden by alg_params (if given there)
optim_method : string
Multi-variable optimisation method
The only tested options are 'fmin' and 'Nelder-mead'
In theory any non-gradient method implemented in
scipy.optimize.mininize could be used.
method_params : dict
Parameters for the optim_method.
Note that where there is an attribute of the
Optimizer object or the termination_conditions matching the key
that attribute. Otherwise, and in some case also,
they are assumed to be method_options
for the scipy.optimize.minimize method.
The commonly used parameter are:
xtol - limit on variable change for convergence
ftol - limit on fidelity error change for convergence
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)
dyn_params : dict
Parameters for the Dynamics object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
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)
prop_params : dict
Parameters for the PropagatorComputer object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
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 FidelityComputer classes for details)
fid_params : dict
Parameters for the FidelityComputer object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
tslot_type : string
Method for computing the dynamics generators, propagators and
evolution in the timeslots.
Options: DEF, UPDATE_ALL, DYNAMIC
UPDATE_ALL is the only one that currently works
(See TimeslotComputer classes for details)
tslot_params : dict
Parameters for the TimeslotComputer object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
guess_pulse_type : string
type / shape of pulse(s) used modulate the control amplitudes.
Options include:
RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW, GAUSSIAN
Default is None
guess_pulse_params : dict
Parameters for the guess pulse generator object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
guess_pulse_action : string
Determines how the guess pulse is applied to the pulse generated
by the basis expansion.
Options are: MODULATE, ADD
Default is MODULATE
pulse_scaling : float
Linear scale factor for generated guess 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
Linear offset for the pulse. That is this value will be added
to any guess pulses generated.
ramping_pulse_type : string
Type of pulse used to modulate the control pulse.
It's intended use for a ramping modulation, which is often required in
experimental setups.
This is only currently implemented in CRAB.
GAUSSIAN_EDGE was added for this purpose.
ramping_pulse_params : dict
Parameters for the ramping pulse generator object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
log_level : integer
level of messaging output from the logger.
Options are attributes of qutip.logging_utils,
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
out_file_ext : string or None
files containing the initial and final control pulse
amplitudes are saved to the current directory.
The default name will be postfixed with this extension
Setting this to None will suppress the output of files
gen_stats : boolean
if set to True then statistics for the optimisation
run will be generated - accessible through attributes
of the stats object
Returns
-------
opt : OptimResult
Returns instance of OptimResult, which has attributes giving the
reason for termination, final fidelity error, final evolution
final amplitudes, statistics etc
"""
# The parameters are checked in create_pulse_optimizer
# so no need to do so here
if log_level == logging.NOTSET:
log_level = logger.getEffectiveLevel()
else:
logger.setLevel(log_level)
# build the algorithm options
if not isinstance(alg_params, dict):
alg_params = {'num_coeffs':num_coeffs,
'init_coeff_scaling':init_coeff_scaling}
else:
if (num_coeffs is not None and
not 'num_coeffs' in alg_params):
alg_params['num_coeffs'] = num_coeffs
if (init_coeff_scaling is not None and
not 'init_coeff_scaling' in alg_params):
alg_params['init_coeff_scaling'] = init_coeff_scaling
# Build the guess pulse options
# Any options passed in the guess_pulse_params take precedence
# over the parameter values.
if guess_pulse_type:
if not isinstance(guess_pulse_params, dict):
guess_pulse_params = {}
if (guess_pulse_scaling is not None and
not 'scaling' in guess_pulse_params):
guess_pulse_params['scaling'] = guess_pulse_scaling
if (guess_pulse_offset is not None and
not 'offset' in guess_pulse_params):
guess_pulse_params['offset'] = guess_pulse_offset
if (guess_pulse_action is not None and
not 'pulse_action' in guess_pulse_params):
guess_pulse_params['pulse_action'] = guess_pulse_action
return optimize_pulse(
drift, ctrls, initial, target,
num_tslots=num_tslots, evo_time=evo_time, tau=tau,
amp_lbound=amp_lbound, amp_ubound=amp_ubound,
fid_err_targ=fid_err_targ, min_grad=0.0,
max_iter=max_iter, max_wall_time=max_wall_time,
alg='CRAB', alg_params=alg_params, optim_params=optim_params,
optim_method=optim_method, method_params=method_params,
dyn_type=dyn_type, dyn_params=dyn_params,
prop_type=prop_type, prop_params=prop_params,
fid_type=fid_type, fid_params=fid_params,
tslot_type=tslot_type, tslot_params=tslot_params,
init_pulse_type=guess_pulse_type,
init_pulse_params=guess_pulse_params,
ramping_pulse_type=ramping_pulse_type,
ramping_pulse_params=ramping_pulse_params,
log_level=log_level, out_file_ext=out_file_ext, gen_stats=gen_stats)
[docs]def opt_pulse_crab_unitary(
H_d, H_c, U_0, U_targ,
num_tslots=None, evo_time=None, tau=None,
amp_lbound=None, amp_ubound=None,
fid_err_targ=1e-5,
max_iter=500, max_wall_time=180,
alg_params=None,
num_coeffs=None, init_coeff_scaling=1.0,
optim_params=None, optim_method='fmin', method_params=None,
phase_option='PSU',
dyn_params=None, prop_params=None, fid_params=None,
tslot_type='DEF', tslot_params=None,
guess_pulse_type=None, guess_pulse_params=None,
guess_pulse_scaling=1.0, guess_pulse_offset=0.0,
guess_pulse_action='MODULATE',
ramping_pulse_type=None, ramping_pulse_params=None,
log_level=logging.NOTSET, out_file_ext=None, gen_stats=False):
"""
Optimise a control pulse to minimise the fidelity error, assuming that
the dynamics of the system are generated by unitary operators.
This function is simply a wrapper for optimize_pulse, where the
appropriate options for unitary dynamics are chosen and the parameter
names are in the format familiar to unitary dynamics
The dynamics of the system in any given timeslot are governed
by the combined Hamiltonian,
i.e. the sum of the H_d + ctrl_amp[j]*H_c[j]
The control pulse is an [n_ts, n_ctrls] array of piecewise amplitudes
The CRAB algorithm uses basis function coefficents as the variables to
optimise. It does NOT use any gradient function.
A multivariable optimisation algorithm attempts to determines the
optimal values for the control pulse to minimise the fidelity error
The fidelity error is some measure of distance of the system evolution
from the given target evolution in the time allowed for the evolution.
Parameters
----------
H_d : Qobj or list of Qobj
Drift (aka system) the underlying Hamiltonian of the system
can provide list (of length num_tslots) for time dependent drift
H_c : List of Qobj or array like [num_tslots, evo_time]
a list of control Hamiltonians. These are scaled by
the amplitudes to alter the overall dynamics
Array like imput can be provided for time dependent control generators
U_0 : Qobj
starting point for the evolution.
Typically the identity matrix
U_targ : 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
max_iter : integer
Maximum number of iterations of the optimisation algorithm
max_wall_time : float
Maximum allowed elapsed time for the optimisation algorithm
alg_params : Dictionary
options that are specific to the algorithm see above
optim_params : Dictionary
The key value pairs are the attribute name and value
used to set attribute values
Note: attributes are created if they do not exist already,
and are overwritten if they do.
Note: method_params are applied afterwards and so may override these
coeff_scaling : float
Linear scale factor for the random basis coefficients
By default these range from -1.0 to 1.0
Note this is overridden by alg_params (if given there)
num_coeffs : integer
Number of coefficients used for each basis function
Note this is calculated automatically based on the dimension of the
dynamics if not given. It is crucial to the performane of the
algorithm that it is set as low as possible, while still giving
high enough frequencies.
Note this is overridden by alg_params (if given there)
optim_method : string
Multi-variable optimisation method
The only tested options are 'fmin' and 'Nelder-mead'
In theory any non-gradient method implemented in
scipy.optimize.mininize could be used.
method_params : dict
Parameters for the optim_method.
Note that where there is an attribute of the
Optimizer object or the termination_conditions matching the key
that attribute. Otherwise, and in some case also,
they are assumed to be method_options
for the scipy.optimize.minimize method.
The commonly used parameter are:
xtol - limit on variable change for convergence
ftol - limit on fidelity error change for convergence
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
dyn_params : dict
Parameters for the Dynamics object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
prop_params : dict
Parameters for the PropagatorComputer object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
fid_params : dict
Parameters for the FidelityComputer object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
tslot_type : string
Method for computing the dynamics generators, propagators and
evolution in the timeslots.
Options: DEF, UPDATE_ALL, DYNAMIC
UPDATE_ALL is the only one that currently works
(See TimeslotComputer classes for details)
tslot_params : dict
Parameters for the TimeslotComputer object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
guess_pulse_type : string
type / shape of pulse(s) used modulate the control amplitudes.
Options include:
RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW, GAUSSIAN
Default is None
guess_pulse_params : dict
Parameters for the guess pulse generator object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
guess_pulse_action : string
Determines how the guess pulse is applied to the pulse generated
by the basis expansion.
Options are: MODULATE, ADD
Default is MODULATE
pulse_scaling : float
Linear scale factor for generated guess 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
Linear offset for the pulse. That is this value will be added
to any guess pulses generated.
ramping_pulse_type : string
Type of pulse used to modulate the control pulse.
It's intended use for a ramping modulation, which is often required in
experimental setups.
This is only currently implemented in CRAB.
GAUSSIAN_EDGE was added for this purpose.
ramping_pulse_params : dict
Parameters for the ramping pulse generator object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
log_level : integer
level of messaging output from the logger.
Options are attributes of qutip.logging_utils,
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
out_file_ext : string or None
files containing the initial and final control pulse
amplitudes are saved to the current directory.
The default name will be postfixed with this extension
Setting this to None will suppress the output of files
gen_stats : boolean
if set to True then statistics for the optimisation
run will be generated - accessible through attributes
of the stats object
Returns
-------
opt : OptimResult
Returns instance of OptimResult, which has attributes giving the
reason for termination, final fidelity error, final evolution
final amplitudes, statistics etc
"""
# The parameters are checked in create_pulse_optimizer
# so no need to do so here
if log_level == logging.NOTSET:
log_level = logger.getEffectiveLevel()
else:
logger.setLevel(log_level)
# build the algorithm options
if not isinstance(alg_params, dict):
alg_params = {'num_coeffs':num_coeffs,
'init_coeff_scaling':init_coeff_scaling}
else:
if (num_coeffs is not None and
not 'num_coeffs' in alg_params):
alg_params['num_coeffs'] = num_coeffs
if (init_coeff_scaling is not None and
not 'init_coeff_scaling' in alg_params):
alg_params['init_coeff_scaling'] = init_coeff_scaling
# Build the guess pulse options
# Any options passed in the guess_pulse_params take precedence
# over the parameter values.
if guess_pulse_type:
if not isinstance(guess_pulse_params, dict):
guess_pulse_params = {}
if (guess_pulse_scaling is not None and
not 'scaling' in guess_pulse_params):
guess_pulse_params['scaling'] = guess_pulse_scaling
if (guess_pulse_offset is not None and
not 'offset' in guess_pulse_params):
guess_pulse_params['offset'] = guess_pulse_offset
if (guess_pulse_action is not None and
not 'pulse_action' in guess_pulse_params):
guess_pulse_params['pulse_action'] = guess_pulse_action
return optimize_pulse_unitary(
H_d, H_c, U_0, U_targ,
num_tslots=num_tslots, evo_time=evo_time, tau=tau,
amp_lbound=amp_lbound, amp_ubound=amp_ubound,
fid_err_targ=fid_err_targ, min_grad=0.0,
max_iter=max_iter, max_wall_time=max_wall_time,
alg='CRAB', alg_params=alg_params, optim_params=optim_params,
optim_method=optim_method, method_params=method_params,
phase_option=phase_option,
dyn_params=dyn_params, prop_params=prop_params, fid_params=fid_params,
tslot_type=tslot_type, tslot_params=tslot_params,
init_pulse_type=guess_pulse_type,
init_pulse_params=guess_pulse_params,
ramping_pulse_type=ramping_pulse_type,
ramping_pulse_params=ramping_pulse_params,
log_level=log_level, out_file_ext=out_file_ext, gen_stats=gen_stats)
[docs]def create_pulse_optimizer(
drift, ctrls, initial, target,
num_tslots=None, evo_time=None, tau=None,
amp_lbound=None, amp_ubound=None,
fid_err_targ=1e-10, min_grad=1e-10,
max_iter=500, max_wall_time=180,
alg='GRAPE', alg_params=None,
optim_params=None, optim_method='DEF', method_params=None,
optim_alg=None, max_metric_corr=None, accuracy_factor=None,
dyn_type='GEN_MAT', dyn_params=None,
prop_type='DEF', prop_params=None,
fid_type='DEF', fid_params=None,
phase_option=None, fid_err_scale_factor=None,
tslot_type='DEF', tslot_params=None,
amp_update_mode=None,
init_pulse_type='DEF', init_pulse_params=None,
pulse_scaling=1.0, pulse_offset=0.0,
ramping_pulse_type=None, ramping_pulse_params=None,
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 or list of Qobj
the underlying dynamics generator of the system
can provide list (of length num_tslots) for time dependent drift
ctrls : List of Qobj or array like [num_tslots, evo_time]
a list of control dynamics generators. These are scaled by
the amplitudes to alter the overall dynamics
Array like imput can be provided for time dependent control generators
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
alg : string
Algorithm to use in pulse optimisation.
Options are:
'GRAPE' (default) - GRadient Ascent Pulse Engineering
'CRAB' - Chopped RAndom Basis
alg_params : Dictionary
options that are specific to the algorithm see above
optim_params : Dictionary
The key value pairs are the attribute name and value
used to set attribute values
Note: attributes are created if they do not exist already,
and are overwritten if they do.
Note: method_params are applied afterwards and so may override these
optim_method : string
a scipy.optimize.minimize method that will be used to optimise
the pulse for minimum fidelity error
Note that FMIN, FMIN_BFGS & FMIN_L_BFGS_B will all result
in calling these specific scipy.optimize methods
Note the LBFGSB is equivalent to FMIN_L_BFGS_B for backwards
capatibility reasons.
Supplying DEF will given alg dependent result:
- GRAPE - Default optim_method is FMIN_L_BFGS_B
- CRAB - Default optim_method is Nelder-Mead
method_params : dict
Parameters for the optim_method.
Note that where there is an attribute of the
Optimizer object or the termination_conditions matching the key
that attribute. Otherwise, and in some case also,
they are assumed to be method_options
for the scipy.optimize.minimize method.
optim_alg : string
Deprecated. Use optim_method.
max_metric_corr : integer
Deprecated. Use method_params instead
accuracy_factor : float
Deprecated. Use method_params instead
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)
dyn_params : dict
Parameters for the Dynamics object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
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)
prop_params : dict
Parameters for the PropagatorComputer object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
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 FidelityComputer classes for details)
fid_params : dict
Parameters for the FidelityComputer object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
phase_option : string
Deprecated. Pass in fid_params instead.
fid_err_scale_factor : float
Deprecated. Use scale_factor key in fid_params instead.
tslot_type : string
Method for computing the dynamics generators, propagators and
evolution in the timeslots.
Options: DEF, UPDATE_ALL, DYNAMIC
UPDATE_ALL is the only one that currently works
(See TimeslotComputer classes for details)
tslot_params : dict
Parameters for the TimeslotComputer object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
amp_update_mode : string
Deprecated. Use tslot_type instead.
init_pulse_type : string
type / shape of pulse(s) used to initialise the
the control amplitudes.
Options (GRAPE) include:
RND, LIN, ZERO, SINE, SQUARE, TRIANGLE, SAW
DEF is RND
(see PulseGen classes for details)
For the CRAB the this the guess_pulse_type.
init_pulse_params : dict
Parameters for the initial / guess pulse generator object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
pulse_scaling : float
Linear scale factor for generated initial / guess 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
Linear offset for the pulse. That is this value will be added
to any initial / guess pulses generated.
ramping_pulse_type : string
Type of pulse used to modulate the control pulse.
It's intended use for a ramping modulation, which is often required in
experimental setups.
This is only currently implemented in CRAB.
GAUSSIAN_EDGE was added for this purpose.
ramping_pulse_params : dict
Parameters for the ramping pulse generator object
The key value pairs are assumed to be attribute name value pairs
They applied after the object is created
log_level : integer
level of messaging output from the logger.
Options are attributes of qutip.logging_utils,
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
gen_stats : boolean
if set to True then statistics for the optimisation
run will be generated - accessible through attributes
of the stats object
Returns
-------
opt : Optimizer
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
ctrls = dynamics._check_ctrls_container(ctrls)
dynamics._check_drift_dyn_gen(drift)
if not isinstance(initial, Qobj):
raise TypeError("initial must be a Qobj")
if not isinstance(target, Qobj):
raise TypeError("target must be a Qobj")
# Deprecated parameter management
if not optim_alg is None:
optim_method = optim_alg
_param_deprecation(
"The 'optim_alg' parameter is deprecated. "
"Use 'optim_method' instead")
if not max_metric_corr is None:
if isinstance(method_params, dict):
if not 'max_metric_corr' in method_params:
method_params['max_metric_corr'] = max_metric_corr
else:
method_params = {'max_metric_corr':max_metric_corr}
_param_deprecation(
"The 'max_metric_corr' parameter is deprecated. "
"Use 'max_metric_corr' in method_params instead")
if not accuracy_factor is None:
if isinstance(method_params, dict):
if not 'accuracy_factor' in method_params:
method_params['accuracy_factor'] = accuracy_factor
else:
method_params = {'accuracy_factor':accuracy_factor}
_param_deprecation(
"The 'accuracy_factor' parameter is deprecated. "
"Use 'accuracy_factor' in method_params instead")
# phase_option
if not phase_option is None:
if isinstance(fid_params, dict):
if not 'phase_option' in fid_params:
fid_params['phase_option'] = phase_option
else:
fid_params = {'phase_option':phase_option}
_param_deprecation(
"The 'phase_option' parameter is deprecated. "
"Use 'phase_option' in fid_params instead")
# fid_err_scale_factor
if not fid_err_scale_factor is None:
if isinstance(fid_params, dict):
if not 'fid_err_scale_factor' in fid_params:
fid_params['scale_factor'] = fid_err_scale_factor
else:
fid_params = {'scale_factor':fid_err_scale_factor}
_param_deprecation(
"The 'fid_err_scale_factor' parameter is deprecated. "
"Use 'scale_factor' in fid_params instead")
# amp_update_mode
if not amp_update_mode is None:
amp_update_mode_up = _upper_safe(amp_update_mode)
if amp_update_mode_up == 'ALL':
tslot_type = 'UPDATE_ALL'
else:
tslot_type = amp_update_mode
_param_deprecation(
"The 'amp_update_mode' parameter is deprecated. "
"Use 'tslot_type' instead")
# set algorithm defaults
alg_up = _upper_safe(alg)
if alg is None:
raise errors.UsageError(
"Optimisation algorithm must be specified through 'alg' parameter")
elif alg_up == 'GRAPE':
if optim_method is None or optim_method.upper() == 'DEF':
optim_method = 'FMIN_L_BFGS_B'
if init_pulse_type is None or init_pulse_type.upper() == 'DEF':
init_pulse_type = 'RND'
elif alg_up == 'CRAB':
if optim_method is None or optim_method.upper() == 'DEF':
optim_method = 'FMIN'
if prop_type is None or prop_type.upper() == 'DEF':
prop_type = 'APPROX'
if init_pulse_type is None or init_pulse_type.upper() == 'DEF':
init_pulse_type = None
else:
raise errors.UsageError(
"No option for pulse optimisation algorithm alg={}".format(alg))
cfg = optimconfig.OptimConfig()
cfg.optim_method = optim_method
cfg.dyn_type = dyn_type
cfg.prop_type = prop_type
cfg.fid_type = fid_type
cfg.init_pulse_type = init_pulse_type
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)
dyn.apply_params(dyn_params)
dyn._drift_dyn_gen_checked = True
dyn._ctrl_dyn_gen_checked = True
# 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)
dyn.prop_computer.apply_params(prop_params)
# Create the FidelityComputer 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
fid_type_up = _upper_safe(fid_type)
if fid_type_up == 'DEF' or fid_type_up is None or fid_type_up == '':
# None given, use the default for the Dynamics
pass
elif fid_type_up == 'TDAPPROX':
if not isinstance(dyn.fid_computer, fidcomp.FidCompTraceDiffApprox):
dyn.fid_computer = fidcomp.FidCompTraceDiffApprox(dyn)
elif fid_type_up == 'TRACEDIFF':
if not isinstance(dyn.fid_computer, fidcomp.FidCompTraceDiff):
dyn.fid_computer = fidcomp.FidCompTraceDiff(dyn)
elif fid_type_up == '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)
dyn.fid_computer.apply_params(fid_params)
# Currently the only working option for tslot computer is
# TSlotCompUpdateAll.
# so just apply the parameters
dyn.tslot_computer.apply_params(tslot_params)
# Create the Optimiser instance
optim_method_up = _upper_safe(optim_method)
if optim_method is None or optim_method_up == '':
raise errors.UsageError("Optimisation method must be specified "
"via 'optim_method' parameter")
elif optim_method_up == 'FMIN_BFGS':
optim = optimizer.OptimizerBFGS(cfg, dyn)
elif optim_method_up == 'LBFGSB' or optim_method_up == 'FMIN_L_BFGS_B':
optim = optimizer.OptimizerLBFGSB(cfg, dyn)
elif optim_method_up == 'FMIN':
if alg_up == 'CRAB':
optim = optimizer.OptimizerCrabFmin(cfg, dyn)
else:
raise errors.UsageError(
"Invalid optim_method '{}' for '{}' algorthim".format(
optim_method, alg))
else:
# Assume that the optim_method is a valid
#scipy.optimize.minimize method
# Choose an optimiser based on the algorithm
if alg_up == 'CRAB':
optim = optimizer.OptimizerCrab(cfg, dyn)
else:
optim = optimizer.Optimizer(cfg, dyn)
optim.alg = alg
optim.method = optim_method
optim.amp_lbound = amp_lbound
optim.amp_ubound = amp_ubound
optim.apply_params(optim_params)
# 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
optim.apply_method_params(method_params)
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
n_ctrls = dyn.num_ctrls
ramping_pgen = None
if ramping_pulse_type:
ramping_pgen = pulsegen.create_pulse_gen(
pulse_type=ramping_pulse_type, dyn=dyn,
pulse_params=ramping_pulse_params)
if alg_up == 'CRAB':
# Create a pulse generator for each ctrl
crab_pulse_params = None
num_coeffs = None
init_coeff_scaling = None
if isinstance(alg_params, dict):
num_coeffs = alg_params.get('num_coeffs')
init_coeff_scaling = alg_params.get('init_coeff_scaling')
if 'crab_pulse_params' in alg_params:
crab_pulse_params = alg_params.get('crab_pulse_params')
guess_pulse_type = init_pulse_type
if guess_pulse_type:
guess_pulse_action = None
guess_pgen = pulsegen.create_pulse_gen(
pulse_type=guess_pulse_type, dyn=dyn)
guess_pgen.scaling = pulse_scaling
guess_pgen.offset = pulse_offset
if init_pulse_params is not None:
guess_pgen.apply_params(init_pulse_params)
guess_pulse_action = init_pulse_params.get('pulse_action')
optim.pulse_generator = []
for j in range(n_ctrls):
crab_pgen = pulsegen.PulseGenCrabFourier(
dyn=dyn, num_coeffs=num_coeffs)
if init_coeff_scaling is not None:
crab_pgen.scaling = init_coeff_scaling
if isinstance(crab_pulse_params, dict):
crab_pgen.apply_params(crab_pulse_params)
lb = None
if amp_lbound:
if isinstance(amp_lbound, list):
try:
lb = amp_lbound[j]
except:
lb = amp_lbound[-1]
else:
lb = amp_lbound
ub = None
if amp_ubound:
if isinstance(amp_ubound, list):
try:
ub = amp_ubound[j]
except:
ub = amp_ubound[-1]
else:
ub = amp_ubound
crab_pgen.lbound = lb
crab_pgen.ubound = ub
if guess_pulse_type:
guess_pgen.lbound = lb
guess_pgen.ubound = ub
crab_pgen.guess_pulse = guess_pgen.gen_pulse()
if guess_pulse_action:
crab_pgen.guess_pulse_action = guess_pulse_action
if ramping_pgen:
crab_pgen.ramping_pulse = ramping_pgen.gen_pulse()
optim.pulse_generator.append(crab_pgen)
#This is just for the debug message now
pgen = optim.pulse_generator[0]
else:
# Create a pulse generator of the type specified
pgen = pulsegen.create_pulse_gen(pulse_type=init_pulse_type, dyn=dyn,
pulse_params=init_pulse_params)
pgen.scaling = pulse_scaling
pgen.offset = pulse_offset
pgen.lbound = amp_lbound
pgen.ubound = amp_ubound
optim.pulse_generator = pgen
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: " + pgen.__class__.__name__)
return optim