Source code for qutip.control.pulseoptim

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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