Source code for qutip.control.dynamics

# -*- coding: utf-8 -*-
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# @author: Alexander Pitchford
# @email1: agp1@aber.ac.uk
# @email2: alex.pitchford@gmail.com
# @organization: Aberystwyth University
# @supervisor: Daniel Burgarth

"""
Classes that define the dynamics of the (quantum) system and target evolution
to be optimised.
The contols are also defined here, i.e. the dynamics generators (Hamiltonians,
Limbladians etc). The dynamics for the time slices are calculated here, along
with the evolution as determined by the control amplitudes.

See the subclass descriptions and choose the appropriate class for the
application. The choice depends on the type of matrix used to define
the dynamics.

These class implement functions for getting the dynamics generators for
the combined (drift + ctrls) dynamics with the approriate operator applied

Note the methods in these classes were inspired by:
DYNAMO - Dynamic Framework for Quantum Optimal Control
See Machnes et.al., arXiv.1011.4874
"""
import warnings
import numpy as np
import scipy.linalg as la
import scipy.sparse as sp
# QuTiP
from qutip.qobj import Qobj
from qutip.sparse import sp_eigs, eigh
import qutip.settings as settings
# QuTiP logging
import qutip.logging_utils as logging
logger = logging.get_logger()
# QuTiP control modules
import qutip.control.errors as errors
import qutip.control.tslotcomp as tslotcomp
import qutip.control.fidcomp as fidcomp
import qutip.control.propcomp as propcomp
import qutip.control.symplectic as sympl
import qutip.control.dump as qtrldump

DEF_NUM_TSLOTS = 10
DEF_EVO_TIME = 1.0

def _is_string(var):
    try:
        if isinstance(var, basestring):
            return True
    except NameError:
        try:
            if isinstance(var, str):
                return True
        except:
            return False
    except:
        return False

    return False

def _check_ctrls_container(ctrls):
    """
    Check through the controls container.
    Convert to an array if its a list of lists
    return the processed container
    raise type error if the container structure is invalid
    """
    if isinstance(ctrls, (list, tuple)):
        # Check to see if list of lists
        try:
            if isinstance(ctrls[0], (list, tuple)):
                ctrls_ = np.empty((len(ctrls), len(ctrls[0])), dtype=object)
                for i, ctrl in enumerate(ctrls):
                    ctrls_[i, :] = ctrl
                ctrls = ctrls_
        except:
            pass

    if isinstance(ctrls, np.ndarray):
        if len(ctrls.shape) != 2:
            raise TypeError("Incorrect shape for ctrl dyn gen array")
        for k in range(ctrls.shape[0]):
            for j in range(ctrls.shape[1]):
                if not isinstance(ctrls[k, j], Qobj):
                    raise TypeError("All control dyn gen must be Qobj")
    elif isinstance(ctrls, (list, tuple)):
        for ctrl in ctrls:
            if not isinstance(ctrl, Qobj):
                raise TypeError("All control dyn gen must be Qobj")
    else:
        raise TypeError("Controls list or array not set correctly")

    return ctrls

def _check_drift_dyn_gen(drift):
    if not isinstance(drift, Qobj):
        if not isinstance(drift, (list, tuple)):
            raise TypeError("drift should be a Qobj or a list of Qobj")
        else:
            for d in drift:
                if not isinstance(d, Qobj):
                    raise TypeError(
                        "drift should be a Qobj or a list of Qobj")

warnings.simplefilter('always', DeprecationWarning) #turn off filter
def _attrib_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 _func_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)

[docs]class Dynamics(object): """ This is a base class only. See subclass descriptions and choose an appropriate one for the application. Note that initialize_controls must be called before most of the methods can be used. init_timeslots can be called sometimes earlier in order to access timeslot related attributes This acts as a container for the operators that are used to calculate time evolution of the system under study. That is the dynamics generators (Hamiltonians, Lindbladians etc), the propagators from one timeslot to the next, and the evolution operators. Due to the large number of matrix additions and multiplications, for small systems at least, the optimisation performance is much better using ndarrays to represent these operators. However Attributes ---------- 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 params: Dictionary The key value pairs are the attribute name and value Note: attributes are created if they do not exist already, and are overwritten if they do. stats : Stats Attributes of which give performance stats for the optimisation set to None to reduce overhead of calculating stats. Note it is (usually) shared with the Optimizer object tslot_computer : TimeslotComputer (subclass instance) Used to manage when the timeslot dynamics generators, propagators, gradients etc are updated prop_computer : PropagatorComputer (subclass instance) Used to compute the propagators and their gradients fid_computer : FidelityComputer (subclass instance) Used to computer the fidelity error and the fidelity error gradient. memory_optimization : int Level of memory optimisation. Setting to 0 (default) means that execution speed is prioritized over memory. Setting to 1 means that some memory prioritisation steps will be taken, for instance using Qobj (and hence sparse arrays) as the the internal operator data type, and not caching some operators Potentially further memory saving maybe made with memory_optimization > 1. The options are processed in _set_memory_optimizations, see this for more information. Individual memory saving options can be switched by settting them directly (see below) oper_dtype : type Data type for internal dynamics generators, propagators and time evolution operators. This can be ndarray or Qobj. Qobj may perform better for larger systems, and will also perform better when (custom) fidelity measures use Qobj methods such as partial trace. See _choose_oper_dtype for how this is chosen when not specified cache_phased_dyn_gen : bool If True then the dynamics generators will be saved with and without the propagation prefactor (if there is one) Defaults to True when memory_optimization=0, otherwise False cache_prop_grad : bool If the True then the propagator gradients (for exact gradients) will be computed when the propagator are computed and cache until the are used by the fidelity computer. If False then the fidelity computer will calculate them as needed. Defaults to True when memory_optimization=0, otherwise False cache_dyn_gen_eigenvectors_adj: bool If True then DynamicsUnitary will cached the adjoint of the Hamiltion eignvector matrix Defaults to True when memory_optimization=0, otherwise False sparse_eigen_decomp: bool If True then DynamicsUnitary will use the sparse eigenvalue decomposition. Defaults to True when memory_optimization<=1, otherwise False num_tslots : integer Number of timeslots (aka timeslices) num_ctrls : integer Number of controls. Note this is calculated as the length of ctrl_dyn_gen when first used. And is recalculated during initialise_controls only. evo_time : float Total time for the evolution tau : array[num_tslots] of float Duration of each timeslot Note that if this is set before initialize_controls is called then num_tslots and evo_time are calculated from tau, otherwise tau is generated from num_tslots and evo_time, that is equal size time slices time : array[num_tslots+1] of float Cumulative time for the evolution, that is the time at the start of each time slice drift_dyn_gen : Qobj or list of Qobj Drift or system dynamics generator (Hamiltonian) Matrix defining the underlying dynamics of the system Can also be a list of Qobj (length num_tslots) for time varying drift dynamics ctrl_dyn_gen : List of Qobj Control dynamics generator (Hamiltonians) List of matrices defining the control dynamics initial : Qobj Starting state / gate The matrix giving the initial state / gate, i.e. at time 0 Typically the identity for gate evolution target : Qobj Target state / gate: The matrix giving the desired state / gate for the evolution ctrl_amps : array[num_tslots, num_ctrls] of float Control amplitudes The amplitude (scale factor) for each control in each timeslot initial_ctrl_scaling : float Scale factor applied to be applied the control amplitudes when they are initialised This is used by the PulseGens rather than in any fucntions in this class initial_ctrl_offset : float Linear offset applied to be applied the control amplitudes when they are initialised This is used by the PulseGens rather than in any fucntions in this class dyn_gen : List of Qobj Dynamics generators the combined drift and control dynamics generators for each timeslot prop : list of Qobj Propagators - used to calculate time evolution from one timeslot to the next prop_grad : array[num_tslots, num_ctrls] of Qobj Propagator gradient (exact gradients only) Array of Qobj that give the gradient with respect to the control amplitudes in a timeslot Note this attribute is only created when the selected PropagatorComputer is an exact gradient type. fwd_evo : List of Qobj Forward evolution (or propagation) the time evolution operator from the initial state / gate to the specified timeslot as generated by the dyn_gen onwd_evo : List of Qobj Onward evolution (or propagation) the time evolution operator from the specified timeslot to end of the evolution time as generated by the dyn_gen onto_evo : List of Qobj 'Backward' List of Qobj propagation the overlap of the onward propagation with the inverse of the target. Note this is only used (so far) by the unitary dynamics fidelity evo_current : Boolean Used to flag that the dynamics used to calculate the evolution operators is current. It is set to False when the amplitudes change fact_mat_round_prec : float Rounding precision used when calculating the factor matrix to determine if two eigenvalues are equivalent Only used when the PropagatorComputer uses diagonalisation def_amps_fname : string Default name for the output used when save_amps is called unitarity_check_level : int If > 0 then unitarity of the system evolution is checked at at evolution recomputation. level 1 checks all propagators level 2 checks eigen basis as well Default is 0 unitarity_tol : Tolerance used in checking if operator is unitary Default is 1e-10 dump : :class:`dump.DynamicsDump` Store of historical calculation data. Set to None (Default) for no storing of historical data Use dumping property to set level of data dumping dumping : string level of data dumping: NONE, SUMMARY, FULL or CUSTOM See property docstring for details dump_to_file : bool If set True then data will be dumped to file during the calculations dumping will be set to SUMMARY during init_evo if dump_to_file is True and dumping not set. Default is False dump_dir : string Basically a link to dump.dump_dir. Exists so that it can be set through dyn_params. If dump is None then will return None or will set dumping to SUMMARY when setting a path """ def __init__(self, optimconfig, params=None): self.config = optimconfig self.params = params self.reset() def reset(self): # Link to optimiser object if self is linked to one self.parent = None # Main functional attributes self.time = None self.initial = None self.target = None self.ctrl_amps = None self.initial_ctrl_scaling = 1.0 self.initial_ctrl_offset = 0.0 self.drift_dyn_gen = None self.ctrl_dyn_gen = None self._tau = None self._evo_time = None self._num_ctrls = None self._num_tslots = None # attributes used for processing evolution self.memory_optimization = 0 self.oper_dtype = None self.cache_phased_dyn_gen = None self.cache_prop_grad = None self.cache_dyn_gen_eigenvectors_adj = None self.sparse_eigen_decomp = None self.dyn_dims = None self.dyn_shape = None self.sys_dims = None self.sys_shape = None self.time_depend_drift = False self.time_depend_ctrl_dyn_gen = False # These internal attributes will be of the internal operator data type # used to compute the evolution # This will be either ndarray or Qobj self._drift_dyn_gen = None self._ctrl_dyn_gen = None self._phased_ctrl_dyn_gen = None self._dyn_gen_phase = None self._phase_application = None self._initial = None self._target = None self._onto_evo_target = None self._dyn_gen = None self._phased_dyn_gen = None self._prop = None self._prop_grad = None self._fwd_evo = None self._onwd_evo = None self._onto_evo = None # The _qobj attribs are Qobj representations of the equivalent # internal attribute. They are only set when the extenal accessors # are used self._onto_evo_target_qobj = None self._dyn_gen_qobj = None self._prop_qobj = None self._prop_grad_qobj = None self._fwd_evo_qobj = None self._onwd_evo_qobj = None self._onto_evo_qobj = None # Atrributes used in diagonalisation # again in internal operator data type (see above) self._decomp_curr = None self._prop_eigen = None self._dyn_gen_eigenvectors = None self._dyn_gen_eigenvectors_adj = None self._dyn_gen_factormatrix = None self.fact_mat_round_prec = 1e-10 # Debug and information attribs self.stats = None self.id_text = 'DYN_BASE' self.def_amps_fname = "ctrl_amps.txt" self.log_level = self.config.log_level # Internal flags self._dyn_gen_mapped = False self._evo_initialized = False self._timeslots_initialized = False self._ctrls_initialized = False self._ctrl_dyn_gen_checked = False self._drift_dyn_gen_checked = False # Unitary checking self.unitarity_check_level = 0 self.unitarity_tol = 1e-10 # Data dumping self.dump = None self.dump_to_file = False self.apply_params() # Create the computing objects self._create_computers() self.clear()
[docs] def apply_params(self, params=None): """ Set object attributes based on the dictionary (if any) passed in the instantiation, or passed as a parameter This is called during the instantiation automatically. The key value pairs are the attribute name and value Note: attributes are created if they do not exist already, and are overwritten if they do. """ if not params: params = self.params if isinstance(params, dict): self.params = params for key in params: setattr(self, key, params[key])
@property def log_level(self): return logger.level @log_level.setter def log_level(self, lvl): """ Set the log_level attribute and set the level of the logger that is call logger.setLevel(lvl) """ logger.setLevel(lvl) @property def dumping(self): """ The level of data dumping that will occur during the time evolution calculation. - NONE : No processing data dumped (Default) - SUMMARY : A summary of each time evolution will be recorded - FULL : All operators used or created in the calculation dumped - CUSTOM : Some customised level of dumping When first set to CUSTOM this is equivalent to SUMMARY. It is then up to the user to specify which operators are dumped. WARNING: FULL could consume a lot of memory! """ if self.dump is None: lvl = 'NONE' else: lvl = self.dump.level return lvl @dumping.setter def dumping(self, value): if value is None: self.dump = None else: if not _is_string(value): raise TypeError("Value must be string value") lvl = value.upper() if lvl == 'NONE': self.dump = None else: if not isinstance(self.dump, qtrldump.DynamicsDump): self.dump = qtrldump.DynamicsDump(self, level=lvl) else: self.dump.level = lvl @property def dump_dir(self): if self.dump: return self.dump.dump_dir else: return None @dump_dir.setter def dump_dir(self, value): if not self.dump: self.dumping = 'SUMMARY' self.dump.dump_dir = value def _create_computers(self): """ Create the default timeslot, fidelity and propagator computers """ # The time slot computer. By default it is set to UpdateAll # can be set to DynUpdate in the configuration # (see class file for details) if self.config.tslot_type == 'DYNAMIC': self.tslot_computer = tslotcomp.TSlotCompDynUpdate(self) else: self.tslot_computer = tslotcomp.TSlotCompUpdateAll(self) self.prop_computer = propcomp.PropCompFrechet(self) self.fid_computer = fidcomp.FidCompTraceDiff(self) def clear(self): self.ctrl_amps = None self.evo_current = False if self.fid_computer is not None: self.fid_computer.clear() @property def num_tslots(self): if not self._timeslots_initialized: self.init_timeslots() return self._num_tslots @num_tslots.setter def num_tslots(self, value): self._num_tslots = value if self._timeslots_initialized: self._tau = None self.init_timeslots() @property def evo_time(self): if not self._timeslots_initialized: self.init_timeslots() return self._evo_time @evo_time.setter def evo_time(self, value): self._evo_time = value if self._timeslots_initialized: self._tau = None self.init_timeslots() @property def tau(self): if not self._timeslots_initialized: self.init_timeslots() return self._tau @tau.setter def tau(self, value): self._tau = value self.init_timeslots()
[docs] def init_timeslots(self): """ Generate the timeslot duration array 'tau' based on the evo_time and num_tslots attributes, unless the tau attribute is already set in which case this step in ignored Generate the cumulative time array 'time' based on the tau values """ # set the time intervals to be equal timeslices of the total if # the have not been set already (as part of user config) if self._num_tslots is None: self._num_tslots = DEF_NUM_TSLOTS if self._evo_time is None: self._evo_time = DEF_EVO_TIME if self._tau is None: self._tau = np.ones(self._num_tslots, dtype='f') * \ self._evo_time/self._num_tslots else: self._num_tslots = len(self._tau) self._evo_time = np.sum(self._tau) self.time = np.zeros(self._num_tslots+1, dtype=float) # set the cumulative time by summing the time intervals for t in range(self._num_tslots): self.time[t+1] = self.time[t] + self._tau[t] self._timeslots_initialized = True
def _set_memory_optimizations(self): """ Set various memory optimisation attributes based on the memory_optimization attribute If they have been set already, e.g. in apply_params then they will not be overridden here """ logger.info("Setting memory optimisations for level {}".format( self.memory_optimization)) if self.oper_dtype is None: self._choose_oper_dtype() logger.info("Internal operator data type choosen to be {}".format( self.oper_dtype)) else: logger.info("Using operator data type {}".format( self.oper_dtype)) if self.cache_phased_dyn_gen is None: if self.memory_optimization > 0: self.cache_phased_dyn_gen = False else: self.cache_phased_dyn_gen = True logger.info("phased dynamics generator caching {}".format( self.cache_phased_dyn_gen)) if self.cache_prop_grad is None: if self.memory_optimization > 0: self.cache_prop_grad = False else: self.cache_prop_grad = True logger.info("propagator gradient caching {}".format( self.cache_prop_grad)) if self.cache_dyn_gen_eigenvectors_adj is None: if self.memory_optimization > 0: self.cache_dyn_gen_eigenvectors_adj = False else: self.cache_dyn_gen_eigenvectors_adj = True logger.info("eigenvector adjoint caching {}".format( self.cache_dyn_gen_eigenvectors_adj)) if self.sparse_eigen_decomp is None: if self.memory_optimization > 1: self.sparse_eigen_decomp = True else: self.sparse_eigen_decomp = False logger.info("use sparse eigen decomp {}".format( self.sparse_eigen_decomp)) def _choose_oper_dtype(self): """ Attempt select most efficient internal operator data type """ if self.memory_optimization > 0: self.oper_dtype = Qobj else: # Method taken from Qobj.expm() # if method is not explicitly given, try to make a good choice # between sparse and dense solvers by considering the size of the # system and the number of non-zero elements. if self.time_depend_drift: dg = self.drift_dyn_gen[0] else: dg = self.drift_dyn_gen if self.time_depend_ctrl_dyn_gen: ctrls = self.ctrl_dyn_gen[0, :] else: ctrls = self.ctrl_dyn_gen for c in ctrls: dg = dg + c N = dg.data.shape[0] n = dg.data.nnz if N ** 2 < 100 * n: # large number of nonzero elements, revert to dense solver self.oper_dtype = np.ndarray elif N > 400: # large system, and quite sparse -> qutips sparse method self.oper_dtype = Qobj else: # small system, but quite sparse -> qutips sparse/dense method self.oper_dtype = np.ndarray return self.oper_dtype def _init_evo(self): """ Create the container lists / arrays for the: dynamics generations, propagators, and evolutions etc Set the time slices and cumulative time """ # check evolution operators if not self._drift_dyn_gen_checked: _check_drift_dyn_gen(self.drift_dyn_gen) if not self._ctrl_dyn_gen_checked: self.ctrl_dyn_gen = _check_ctrls_container(self.ctrl_dyn_gen) if not isinstance(self.initial, Qobj): raise TypeError("initial must be a Qobj") if not isinstance(self.target, Qobj): raise TypeError("target must be a Qobj") self.refresh_drift_attribs() self.sys_dims = self.initial.dims self.sys_shape = self.initial.shape # Set the phase application method self._init_phase() self._set_memory_optimizations() if self.sparse_eigen_decomp and self.sys_shape[0] <= 2: raise ValueError( "Single qubit pulse optimization dynamics cannot use sparse" " eigenvector decomposition because of limitations in" " scipy.linalg.eigsh. Pleae set sparse_eigen_decomp to False" " or increase the size of the system.") n_ts = self.num_tslots n_ctrls = self.num_ctrls if self.oper_dtype == Qobj: self._initial = self.initial self._target = self.target self._drift_dyn_gen = self.drift_dyn_gen self._ctrl_dyn_gen = self.ctrl_dyn_gen elif self.oper_dtype == np.ndarray: self._initial = self.initial.full() self._target = self.target.full() if self.time_depend_drift: self._drift_dyn_gen = [d.full() for d in self.drift_dyn_gen] else: self._drift_dyn_gen = self.drift_dyn_gen.full() if self.time_depend_ctrl_dyn_gen: self._ctrl_dyn_gen = np.empty([n_ts, n_ctrls], dtype=object) for k in range(n_ts): for j in range(n_ctrls): self._ctrl_dyn_gen[k, j] = \ self.ctrl_dyn_gen[k, j].full() else: self._ctrl_dyn_gen = [ctrl.full() for ctrl in self.ctrl_dyn_gen] else: raise ValueError( "Unknown oper_dtype {!r}. The oper_dtype may be qutip.Qobj or" " numpy.ndarray.".format(self.oper_dtype)) if self.cache_phased_dyn_gen: if self.time_depend_ctrl_dyn_gen: self._phased_ctrl_dyn_gen = np.empty([n_ts, n_ctrls], dtype=object) for k in range(n_ts): for j in range(n_ctrls): self._phased_ctrl_dyn_gen[k, j] = self._apply_phase( self._ctrl_dyn_gen[k, j]) else: self._phased_ctrl_dyn_gen = [self._apply_phase(ctrl) for ctrl in self._ctrl_dyn_gen] self._dyn_gen = [object for x in range(self.num_tslots)] if self.cache_phased_dyn_gen: self._phased_dyn_gen = [object for x in range(self.num_tslots)] self._prop = [object for x in range(self.num_tslots)] if self.prop_computer.grad_exact and self.cache_prop_grad: self._prop_grad = np.empty([self.num_tslots, self.num_ctrls], dtype=object) # Time evolution operator (forward propagation) self._fwd_evo = [object for x in range(self.num_tslots+1)] self._fwd_evo[0] = self._initial if self.fid_computer.uses_onwd_evo: # Time evolution operator (onward propagation) self._onwd_evo = [object for x in range(self.num_tslots)] if self.fid_computer.uses_onto_evo: # Onward propagation overlap with inverse target self._onto_evo = [object for x in range(self.num_tslots+1)] self._onto_evo[self.num_tslots] = self._get_onto_evo_target() if isinstance(self.prop_computer, propcomp.PropCompDiag): self._create_decomp_lists() if (self.log_level <= logging.DEBUG and isinstance(self, DynamicsUnitary)): self.unitarity_check_level = 1 if self.dump_to_file: if self.dump is None: self.dumping = 'SUMMARY' self.dump.write_to_file = True self.dump.create_dump_dir() logger.info("Dynamics dump will be written to:\n{}".format( self.dump.dump_dir)) self._evo_initialized = True @property def dyn_gen_phase(self): """ Some op that is applied to the dyn_gen before expontiating to get the propagator. See `phase_application` for how this is applied """ # Note that if this returns None then _apply_phase will never be # called return self._dyn_gen_phase @dyn_gen_phase.setter def dyn_gen_phase(self, value): self._dyn_gen_phase = value @property def phase_application(self): """ phase_application : scalar(string), default='preop' Determines how the phase is applied to the dynamics generators - 'preop' : P = expm(phase*dyn_gen) - 'postop' : P = expm(dyn_gen*phase) - 'custom' : Customised phase application The 'custom' option assumes that the _apply_phase method has been set to a custom function. """ return self._phase_application @phase_application.setter def phase_application(self, value): self._set_phase_application(value) def _set_phase_application(self, value): self._config_phase_application(value) self._phase_application = value def _config_phase_application(self, ph_app=None): """ Set the appropriate function for the phase application """ err_msg = ("Invalid value '{}' for phase application. Must be either " "'preop', 'postop' or 'custom'".format(ph_app)) if ph_app is None: ph_app = self._phase_application try: ph_app = ph_app.lower() except: raise ValueError(err_msg) if ph_app == 'preop': self._apply_phase = self._apply_phase_preop elif ph_app == 'postop': self._apply_phase = self._apply_phase_postop elif ph_app == 'custom': # Do nothing, assume _apply_phase set elsewhere pass else: raise ValueError(err_msg) def _init_phase(self): if self.dyn_gen_phase is not None: self._config_phase_application() else: self.cache_phased_dyn_gen = False def _apply_phase(self, dg): """ This default method does nothing. It will be set to another method automatically if `phase_application` is 'preop' or 'postop'. It should be overridden repointed if `phase_application` is 'custom' It will never be called if `dyn_gen_phase` is None """ return dg def _apply_phase_preop(self, dg): """ Apply phasing operator to dynamics generator. This called during the propagator calculation. In this case it will be applied as phase*dg """ if hasattr(self.dyn_gen_phase, 'dot'): phased_dg = self._dyn_gen_phase.dot(dg) else: phased_dg = self._dyn_gen_phase*dg return phased_dg def _apply_phase_postop(self, dg): """ Apply phasing operator to dynamics generator. This called during the propagator calculation. In this case it will be applied as dg*phase """ if hasattr(self.dyn_gen_phase, 'dot'): phased_dg = dg.dot(self._dyn_gen_phase) else: phased_dg = dg*self._dyn_gen_phase return phased_dg def _create_decomp_lists(self): """ Create lists that will hold the eigen decomposition used in calculating propagators and gradients Note: used with PropCompDiag propagator calcs """ n_ts = self.num_tslots self._decomp_curr = [False for x in range(n_ts)] self._prop_eigen = [object for x in range(n_ts)] self._dyn_gen_eigenvectors = [object for x in range(n_ts)] if self.cache_dyn_gen_eigenvectors_adj: self._dyn_gen_eigenvectors_adj = [object for x in range(n_ts)] self._dyn_gen_factormatrix = [object for x in range(n_ts)]
[docs] def initialize_controls(self, amps, init_tslots=True): """ Set the initial control amplitudes and time slices Note this must be called after the configuration is complete before any dynamics can be calculated """ if not isinstance(self.prop_computer, propcomp.PropagatorComputer): raise errors.UsageError( "No prop_computer (propagator computer) " "set. A default should be assigned by the Dynamics subclass") if not isinstance(self.tslot_computer, tslotcomp.TimeslotComputer): raise errors.UsageError( "No tslot_computer (Timeslot computer)" " set. A default should be assigned by the Dynamics class") if not isinstance(self.fid_computer, fidcomp.FidelityComputer): raise errors.UsageError( "No fid_computer (Fidelity computer)" " set. A default should be assigned by the Dynamics subclass") self.ctrl_amps = None if not self._timeslots_initialized: init_tslots = True if init_tslots: self.init_timeslots() self._init_evo() self.tslot_computer.init_comp() self.fid_computer.init_comp() self._ctrls_initialized = True self.update_ctrl_amps(amps)
def check_ctrls_initialized(self): if not self._ctrls_initialized: raise errors.UsageError( "Controls not initialised. " "Ensure Dynamics.initialize_controls has been " "executed with the initial control amplitudes.") def get_amp_times(self): return self.time[:self.num_tslots]
[docs] def save_amps(self, file_name=None, times=None, amps=None, verbose=False): """ Save a file with the current control amplitudes in each timeslot The first column in the file will be the start time of the slot Parameters ---------- file_name : string Name of the file If None given the def_amps_fname attribuite will be used times : List type (or string) List / array of the start times for each slot If None given this will be retrieved through get_amp_times() If 'exclude' then times will not be saved in the file, just the amplitudes amps : Array[num_tslots, num_ctrls] Amplitudes to be saved If None given the ctrl_amps attribute will be used verbose : Boolean If True then an info message will be logged """ self.check_ctrls_initialized() inctimes = True if file_name is None: file_name = self.def_amps_fname if amps is None: amps = self.ctrl_amps if times is None: times = self.get_amp_times() else: if _is_string(times): if times.lower() == 'exclude': inctimes = False else: logger.warn("Unknown option for times '{}' " "when saving amplitudes".format(times)) times = self.get_amp_times() try: if inctimes: shp = amps.shape data = np.empty([shp[0], shp[1] + 1], dtype=float) data[:, 0] = times data[:, 1:] = amps else: data = amps np.savetxt(file_name, data, delimiter='\t', fmt='%14.6g') if verbose: logger.info("Amplitudes saved to file: " + file_name) except Exception as e: logger.error("Failed to save amplitudes due to underling " "error: {}".format(e))
[docs] def update_ctrl_amps(self, new_amps): """ Determine if any amplitudes have changed. If so, then mark the timeslots as needing recalculation The actual work is completed by the compare_amps method of the timeslot computer """ if self.log_level <= logging.DEBUG_INTENSE: logger.log(logging.DEBUG_INTENSE, "Updating amplitudes...\n" "Current control amplitudes:\n" + str(self.ctrl_amps) + "\n(potenially) new amplitudes:\n" + str(new_amps)) self.tslot_computer.compare_amps(new_amps)
[docs] def flag_system_changed(self): """ Flag evolution, fidelity and gradients as needing recalculation """ self.evo_current = False self.fid_computer.flag_system_changed()
[docs] def get_drift_dim(self): """ Returns the size of the matrix that defines the drift dynamics that is assuming the drift is NxN, then this returns N """ if self.dyn_shape is None: self.refresh_drift_attribs() return self.dyn_shape[0]
[docs] def refresh_drift_attribs(self): """Reset the dyn_shape, dyn_dims and time_depend_drift attribs""" if isinstance(self.drift_dyn_gen, (list, tuple)): d0 = self.drift_dyn_gen[0] self.time_depend_drift = True else: d0 = self.drift_dyn_gen self.time_depend_drift = False if not isinstance(d0, Qobj): raise TypeError("Unable to determine drift attributes, " "because drift_dyn_gen is not Qobj (nor list of)") self.dyn_shape = d0.shape self.dyn_dims = d0.dims
[docs] def get_num_ctrls(self): """ calculate the of controls from the length of the control list sets the num_ctrls property, which can be used alternatively subsequently """ _func_deprecation("'get_num_ctrls' has been replaced by " "'num_ctrls' property") return self.num_ctrls
def _get_num_ctrls(self): if not self._ctrl_dyn_gen_checked: self.ctrl_dyn_gen = _check_ctrls_container(self.ctrl_dyn_gen) self._ctrl_dyn_gen_checked = True if isinstance(self.ctrl_dyn_gen, np.ndarray): self._num_ctrls = self.ctrl_dyn_gen.shape[1] self.time_depend_ctrl_dyn_gen = True else: self._num_ctrls = len(self.ctrl_dyn_gen) return self._num_ctrls @property def num_ctrls(self): """ calculate the of controls from the length of the control list sets the num_ctrls property, which can be used alternatively subsequently """ if self._num_ctrls is None: self._num_ctrls = self._get_num_ctrls() return self._num_ctrls @property def onto_evo_target(self): if self._onto_evo_target is None: self._get_onto_evo_target() if self._onto_evo_target_qobj is None: if isinstance(self._onto_evo_target, Qobj): self._onto_evo_target_qobj = self._onto_evo_target else: rev_dims = [self.sys_dims[1], self.sys_dims[0]] self._onto_evo_target_qobj = Qobj(self._onto_evo_target, dims=rev_dims) return self._onto_evo_target_qobj def get_owd_evo_target(self): _func_deprecation("'get_owd_evo_target' has been replaced by " "'onto_evo_target' property") return self.onto_evo_target def _get_onto_evo_target(self): """ Get the inverse of the target. Used for calculating the 'onto target' evolution This is actually only relevant for unitary dynamics where the target.dag() is what is required However, for completeness, in general the inverse of the target operator is is required For state-to-state, the bra corresponding to the is required ket """ if self.target.shape[0] == self.target.shape[1]: #Target is operator targ = la.inv(self.target.full()) if self.oper_dtype == Qobj: rev_dims = [self.target.dims[1], self.target.dims[0]] self._onto_evo_target = Qobj(targ, dims=rev_dims) elif self.oper_dtype == np.ndarray: self._onto_evo_target = targ else: assert False, f"Unknown oper_dtype {self.oper_dtype!r}" else: if self.oper_dtype == Qobj: self._onto_evo_target = self.target.dag() elif self.oper_dtype == np.ndarray: self._onto_evo_target = self.target.dag().full() else: assert False, f"Unknown oper_dtype {self.oper_dtype!r}" return self._onto_evo_target
[docs] def combine_dyn_gen(self, k): """ Computes the dynamics generator for a given timeslot The is the combined Hamiltion for unitary systems """ _func_deprecation("'combine_dyn_gen' has been replaced by " "'_combine_dyn_gen'") self._combine_dyn_gen(k) return self._dyn_gen(k)
def _combine_dyn_gen(self, k): """ Computes the dynamics generator for a given timeslot The is the combined Hamiltion for unitary systems Also applies the phase (if any required by the propagation) """ if self.time_depend_drift: dg = self._drift_dyn_gen[k] else: dg = self._drift_dyn_gen for j in range(self._num_ctrls): if self.time_depend_ctrl_dyn_gen: dg = dg + self.ctrl_amps[k, j]*self._ctrl_dyn_gen[k, j] else: dg = dg + self.ctrl_amps[k, j]*self._ctrl_dyn_gen[j] self._dyn_gen[k] = dg if self.cache_phased_dyn_gen: self._phased_dyn_gen[k] = self._apply_phase(dg)
[docs] def get_dyn_gen(self, k): """ Get the combined dynamics generator for the timeslot Not implemented in the base class. Choose a subclass """ _func_deprecation("'get_dyn_gen' has been replaced by " "'_get_phased_dyn_gen'") return self._get_phased_dyn_gen(k)
def _get_phased_dyn_gen(self, k): if self.dyn_gen_phase is None: return self._dyn_gen[k] else: if self._phased_dyn_gen is None: return self._apply_phase(self._dyn_gen[k]) else: return self._phased_dyn_gen[k]
[docs] def get_ctrl_dyn_gen(self, j): """ Get the dynamics generator for the control Not implemented in the base class. Choose a subclass """ _func_deprecation("'get_ctrl_dyn_gen' has been replaced by " "'_get_phased_ctrl_dyn_gen'") return self._get_phased_ctrl_dyn_gen(0, j)
def _get_phased_ctrl_dyn_gen(self, k, j): if self._phased_ctrl_dyn_gen is not None: if self.time_depend_ctrl_dyn_gen: return self._phased_ctrl_dyn_gen[k, j] else: return self._phased_ctrl_dyn_gen[j] else: if self.time_depend_ctrl_dyn_gen: if self.dyn_gen_phase is None: return self._ctrl_dyn_gen[k, j] else: return self._apply_phase(self._ctrl_dyn_gen[k, j]) else: if self.dyn_gen_phase is None: return self._ctrl_dyn_gen[j] else: return self._apply_phase(self._ctrl_dyn_gen[j]) @property def dyn_gen(self): """ List of combined dynamics generators (Qobj) for each timeslot """ if self._dyn_gen is not None: if self._dyn_gen_qobj is None: if self.oper_dtype == Qobj: self._dyn_gen_qobj = self._dyn_gen else: self._dyn_gen_qobj = [Qobj(dg, dims=self.dyn_dims) for dg in self._dyn_gen] return self._dyn_gen_qobj @property def prop(self): """ List of propagators (Qobj) for each timeslot """ if self._prop is not None: if self._prop_qobj is None: if self.oper_dtype == Qobj: self._prop_qobj = self._prop else: self._prop_qobj = [Qobj(dg, dims=self.dyn_dims) for dg in self._prop] return self._prop_qobj @property def prop_grad(self): """ Array of propagator gradients (Qobj) for each timeslot, control """ if self._prop_grad is not None: if self._prop_grad_qobj is None: if self.oper_dtype == Qobj: self._prop_grad_qobj = self._prop_grad else: self._prop_grad_qobj = np.empty( [self.num_tslots, self.num_ctrls], dtype=object) for k in range(self.num_tslots): for j in range(self.num_ctrls): self._prop_grad_qobj[k, j] = Qobj( self._prop_grad[k, j], dims=self.dyn_dims) return self._prop_grad_qobj def _get_prop_grad(self, k, j): if self.cache_prop_grad: prop_grad = self._prop_grad[k, j] else: prop_grad = self.prop_computer._compute_prop_grad(k, j, compute_prop = False) return prop_grad @property def evo_init2t(self): _attrib_deprecation( "'evo_init2t' has been replaced by '_fwd_evo'") return self._fwd_evo @property def fwd_evo(self): """ List of evolution operators (Qobj) from the initial to the given timeslot """ if self._fwd_evo is not None: if self._fwd_evo_qobj is None: if self.oper_dtype == Qobj: self._fwd_evo_qobj = self._fwd_evo else: self._fwd_evo_qobj = [self.initial] for k in range(1, self.num_tslots+1): self._fwd_evo_qobj.append(Qobj(self._fwd_evo[k], dims=self.sys_dims)) return self._fwd_evo_qobj def _get_full_evo(self): return self._fwd_evo[self._num_tslots] @property def full_evo(self): """Full evolution - time evolution at final time slot""" return self.fwd_evo[self.num_tslots] @property def evo_t2end(self): _attrib_deprecation( "'evo_t2end' has been replaced by '_onwd_evo'") return self._onwd_evo @property def onwd_evo(self): """ List of evolution operators (Qobj) from the initial to the given timeslot """ if self._onwd_evo is not None: if self._onwd_evo_qobj is None: if self.oper_dtype == Qobj: self._onwd_evo_qobj = self._fwd_evo else: self._onwd_evo_qobj = [Qobj(dg, dims=self.sys_dims) for dg in self._onwd_evo] return self._onwd_evo_qobj @property def evo_t2targ(self): _attrib_deprecation( "'evo_t2targ' has been replaced by '_onto_evo'") return self._onto_evo @property def onto_evo(self): """ List of evolution operators (Qobj) from the initial to the given timeslot """ if self._onto_evo is not None: if self._onto_evo_qobj is None: if self.oper_dtype == Qobj: self._onto_evo_qobj = self._onto_evo else: self._onto_evo_qobj = [] for k in range(0, self.num_tslots): self._onto_evo_qobj.append(Qobj(self._onto_evo[k], dims=self.sys_dims)) self._onto_evo_qobj.append(self.onto_evo_target) return self._onto_evo_qobj
[docs] def compute_evolution(self): """ Recalculate the time evolution operators Dynamics generators (e.g. Hamiltonian) and prop (propagators) are calculated as necessary Actual work is completed by the recompute_evolution method of the timeslot computer """ # Check if values are already current, otherwise calculate all values if not self.evo_current: if self.log_level <= logging.DEBUG_VERBOSE: logger.log(logging.DEBUG_VERBOSE, "Computing evolution") self.tslot_computer.recompute_evolution() self.evo_current = True return True else: return False
def _ensure_decomp_curr(self, k): """ Checks to see if the diagonalisation has been completed since the last update of the dynamics generators (after the amplitude update) If not then the diagonlisation is completed """ if self._decomp_curr is None: raise errors.UsageError("Decomp lists have not been created") if not self._decomp_curr[k]: self._spectral_decomp(k) def _spectral_decomp(self, k): """ Calculate the diagonalization of the dynamics generator generating lists of eigenvectors, propagators in the diagonalised basis, and the 'factormatrix' used in calculating the propagator gradient Not implemented in this base class, because the method is specific to the matrix type """ raise errors.UsageError("Decomposition cannot be completed by " "this class. Try a(nother) subclass") def _is_unitary(self, A): """ Checks whether operator A is unitary A can be either Qobj or ndarray """ if isinstance(A, Qobj): unitary = np.allclose(np.eye(A.shape[0]), A*A.dag().full(), atol=self.unitarity_tol) else: unitary = np.allclose(np.eye(len(A)), A.dot(A.T.conj()), atol=self.unitarity_tol) return unitary def _calc_unitary_err(self, A): if isinstance(A, Qobj): err = np.sum(abs(np.eye(A.shape[0]) - A*A.dag().full())) else: err = np.sum(abs(np.eye(len(A)) - A.dot(A.T.conj()))) return err
[docs] def unitarity_check(self): """ Checks whether all propagators are unitary """ for k in range(self.num_tslots): if not self._is_unitary(self._prop[k]): logger.warning( "Progator of timeslot {} is not unitary".format(k))
[docs]class DynamicsGenMat(Dynamics): """ This sub class can be used for any system where no additional operator is applied to the dynamics generator before calculating the propagator, e.g. classical dynamics, Lindbladian """ def reset(self): Dynamics.reset(self) self.id_text = 'GEN_MAT' self.apply_params()
[docs]class DynamicsUnitary(Dynamics): """ This is the subclass to use for systems with dynamics described by unitary matrices. E.g. closed systems with Hermitian Hamiltonians Note a matrix diagonalisation is used to compute the exponent The eigen decomposition is also used to calculate the propagator gradient. The method is taken from DYNAMO (see file header) Attributes ---------- drift_ham : Qobj This is the drift Hamiltonian for unitary dynamics It is mapped to drift_dyn_gen during initialize_controls ctrl_ham : List of Qobj These are the control Hamiltonians for unitary dynamics It is mapped to ctrl_dyn_gen during initialize_controls H : List of Qobj The combined drift and control Hamiltonians for each timeslot These are the dynamics generators for unitary dynamics. It is mapped to dyn_gen during initialize_controls """ def reset(self): Dynamics.reset(self) self.id_text = 'UNIT' self.drift_ham = None self.ctrl_ham = None self.H = None self._dyn_gen_phase = -1j self._phase_application = 'preop' self.apply_params() def _create_computers(self): """ Create the default timeslot, fidelity and propagator computers """ # The time slot computer. By default it is set to _UpdateAll # can be set to _DynUpdate in the configuration # (see class file for details) if self.config.tslot_type == 'DYNAMIC': self.tslot_computer = tslotcomp.TSlotCompDynUpdate(self) else: self.tslot_computer = tslotcomp.TSlotCompUpdateAll(self) # set the default fidelity computer self.fid_computer = fidcomp.FidCompUnitary(self) # set the default propagator computer self.prop_computer = propcomp.PropCompDiag(self)
[docs] def initialize_controls(self, amplitudes, init_tslots=True): # Either the _dyn_gen or _ham names can be used # This assumes that one or other has been set in the configuration self._map_dyn_gen_to_ham() Dynamics.initialize_controls(self, amplitudes, init_tslots=init_tslots)
#self.H = self._dyn_gen def _map_dyn_gen_to_ham(self): if self.drift_dyn_gen is None: self.drift_dyn_gen = self.drift_ham else: self.drift_ham = self.drift_dyn_gen if self.ctrl_dyn_gen is None: self.ctrl_dyn_gen = self.ctrl_ham else: self.ctrl_ham = self.ctrl_dyn_gen self._dyn_gen_mapped = True @property def num_ctrls(self): if not self._dyn_gen_mapped: self._map_dyn_gen_to_ham() if self._num_ctrls is None: self._num_ctrls = self._get_num_ctrls() return self._num_ctrls def _get_onto_evo_target(self): """ Get the adjoint of the target. Used for calculating the 'backward' evolution """ if self.oper_dtype == Qobj: self._onto_evo_target = self.target.dag() else: self._onto_evo_target = self._target.T.conj() return self._onto_evo_target def _spectral_decomp(self, k): """ Calculates the diagonalization of the dynamics generator generating lists of eigenvectors, propagators in the diagonalised basis, and the 'factormatrix' used in calculating the propagator gradient """ if self.oper_dtype == Qobj: H = self._dyn_gen[k] # Returns eigenvalues as array (row) # and eigenvectors as rows of an array eig_val, eig_vec = sp_eigs(H.data, H.isherm, sparse=self.sparse_eigen_decomp) eig_vec = eig_vec.T if self.sparse_eigen_decomp: # when sparse=True, sp_eigs returns an ndarray where each # element is a sparse matrix so we convert it into a sparse # matrix we can later pass to Qobj(...) eig_vec = sp.hstack(eig_vec) elif self.oper_dtype == np.ndarray: H = self._dyn_gen[k] # returns row vector of eigenvals, columns with the eigenvecs eig_val, eig_vec = eigh(H) else: assert False, f"Unknown oper_dtype {self.oper_dtype!r}" # assuming H is an nxn matrix, find n n = self.get_drift_dim() # Calculate the propagator in the diagonalised basis eig_val_tau = -1j*eig_val*self.tau[k] prop_eig = np.exp(eig_val_tau) # Generate the factor matrix through the differences # between each of the eigenvectors and the exponentiations # create nxn matrix where each eigen val is repeated n times # down the columns o = np.ones([n, n]) eig_val_cols = eig_val_tau*o # calculate all the differences by subtracting it from its transpose eig_val_diffs = eig_val_cols - eig_val_cols.T # repeat for the propagator prop_eig_cols = prop_eig*o prop_eig_diffs = prop_eig_cols - prop_eig_cols.T # the factor matrix is the elementwise quotient of the # differeneces between the exponentiated eigen vals and the # differences between the eigen vals # need to avoid division by zero that would arise due to denegerate # eigenvalues and the diagonals degen_mask = np.abs(eig_val_diffs) < self.fact_mat_round_prec eig_val_diffs[degen_mask] = 1 factors = prop_eig_diffs / eig_val_diffs # for degenerate eigenvalues the factor is just the exponent factors[degen_mask] = prop_eig_cols[degen_mask] # Store eigenvectors, propagator and factor matric # for use in propagator computations self._decomp_curr[k] = True if isinstance(factors, np.ndarray): self._dyn_gen_factormatrix[k] = factors else: self._dyn_gen_factormatrix[k] = np.array(factors) if self.oper_dtype == Qobj: self._prop_eigen[k] = Qobj(np.diagflat(prop_eig), dims=self.dyn_dims) self._dyn_gen_eigenvectors[k] = Qobj(eig_vec, dims=self.dyn_dims) # The _dyn_gen_eigenvectors_adj list is not used in # memory optimised modes if self._dyn_gen_eigenvectors_adj is not None: self._dyn_gen_eigenvectors_adj[k] = \ self._dyn_gen_eigenvectors[k].dag() elif self.oper_dtype == np.ndarray: self._prop_eigen[k] = np.diagflat(prop_eig) self._dyn_gen_eigenvectors[k] = eig_vec # The _dyn_gen_eigenvectors_adj list is not used in # memory optimised modes if self._dyn_gen_eigenvectors_adj is not None: self._dyn_gen_eigenvectors_adj[k] = \ self._dyn_gen_eigenvectors[k].conj().T else: assert False, f"Unknown oper_dtype {self.oper_dtype!r}" def _get_dyn_gen_eigenvectors_adj(self, k): # The _dyn_gen_eigenvectors_adj list is not used in # memory optimised modes if self._dyn_gen_eigenvectors_adj is not None: return self._dyn_gen_eigenvectors_adj[k] else: if self.oper_dtype == Qobj: return self._dyn_gen_eigenvectors[k].dag() else: return self._dyn_gen_eigenvectors[k].conj().T
[docs] def check_unitarity(self): """ Checks whether all propagators are unitary For propagators found not to be unitary, the potential underlying causes are investigated. """ for k in range(self.num_tslots): prop_unit = self._is_unitary(self._prop[k]) if not prop_unit: logger.warning( "Progator of timeslot {} is not unitary".format(k)) if not prop_unit or self.unitarity_check_level > 1: # Check Hamiltonian H = self._dyn_gen[k] if isinstance(H, Qobj): herm = H.isherm else: diff = np.abs(H.T.conj() - H) herm = False if np.any(diff > settings.atol) else True eigval_unit = self._is_unitary(self._prop_eigen[k]) eigvec_unit = self._is_unitary(self._dyn_gen_eigenvectors[k]) if self._dyn_gen_eigenvectors_adj is not None: eigvecadj_unit = self._is_unitary( self._dyn_gen_eigenvectors_adj[k]) else: eigvecadj_unit = None msg = ("prop unit: {}; H herm: {}; " "eigval unit: {}; eigvec unit: {}; " "eigvecadj_unit: {}".format( prop_unit, herm, eigval_unit, eigvec_unit, eigvecadj_unit)) logger.info(msg)
[docs]class DynamicsSymplectic(Dynamics): """ Symplectic systems This is the subclass to use for systems where the dynamics is described by symplectic matrices, e.g. coupled oscillators, quantum optics Attributes ---------- omega : array[drift_dyn_gen.shape] matrix used in the calculation of propagators (time evolution) with symplectic systems. """ def reset(self): Dynamics.reset(self) self.id_text = 'SYMPL' self._omega = None self._omega_qobj = None self._phase_application = 'postop' self.grad_exact = True self.apply_params() def _create_computers(self): """ Create the default timeslot, fidelity and propagator computers """ # The time slot computer. By default it is set to _UpdateAll # can be set to _DynUpdate in the configuration # (see class file for details) if self.config.tslot_type == 'DYNAMIC': self.tslot_computer = tslotcomp.TSlotCompDynUpdate(self) else: self.tslot_computer = tslotcomp.TSlotCompUpdateAll(self) self.prop_computer = propcomp.PropCompFrechet(self) self.fid_computer = fidcomp.FidCompTraceDiff(self) @property def omega(self): if self._omega is None: self._get_omega() if self._omega_qobj is None: self._omega_qobj = Qobj(self._omega, dims=self.dyn_dims) return self._omega_qobj def _get_omega(self): if self._omega is None: n = self.get_drift_dim() // 2 omg = sympl.calc_omega(n) if self.oper_dtype == Qobj: self._omega = Qobj(omg, dims=self.dyn_dims) self._omega_qobj = self._omega else: self._omega = omg return self._omega def _set_phase_application(self, value): Dynamics._set_phase_application(self, value) if self._evo_initialized: phase = self._get_dyn_gen_phase() if phase is not None: self._dyn_gen_phase = phase def _get_dyn_gen_phase(self): if self._phase_application == 'postop': phase = -self._get_omega() elif self._phase_application == 'preop': phase = self._get_omega() elif self._phase_application == 'custom': phase = None # Assume phase set by user else: raise ValueError("No option for phase_application " "'{}'".format(self._phase_application)) return phase @property def dyn_gen_phase(self): r""" The phasing operator for the symplectic group generators usually refered to as \Omega By default this is applied as 'postop' dyn_gen*-\Omega If phase_application is 'preop' it is applied as \Omega*dyn_gen """ # Cannot be calculated until the dyn_shape is set # that is after the drift dyn gen has been set. if self._dyn_gen_phase is None: self._dyn_gen_phase = self._get_dyn_gen_phase() return self._dyn_gen_phase