Source code for qutip.control.dynamics

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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 os
import numpy as np
import scipy.linalg as la
from six import string_types
# QuTiP logging
import qutip.logging 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


[docs]class Dynamics: """ 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 any of the methods can be used. Attributes ---------- log_level : integer level of messaging output from the logger. Options are attributes of qutip.logging, in decreasing levels of messaging, are: DEBUG_INTENSE, DEBUG_VERBOSE, DEBUG, INFO, WARN, ERROR, CRITICAL Anything WARN or above is effectively 'quiet' execution, assuming everything runs as expected. The default NOTSET implies that the level will be taken from the QuTiP settings file, which by default is WARN Note value should be set using set_log_level 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. num_tslots : integer Number of timeslots, aka timeslices num_ctrls : integer Number of controls. Note this is set when get_num_ctrls is called based on the length of ctrl_dyn_gen 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 Drift or system dynamics generator Matrix defining the underlying dynamics of the system ctrl_dyn_gen : List of Qobj Control dynamics generator: ctrl_dyn_gen () 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 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 self.initial_ctrl_offset = 0.0 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 matrices 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. evo_init2t : 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 evo_t2end : 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 evo_t2targ : 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 decomp_curr : List of boolean Indicates whether the diagonalisation for the timeslot is fresh, it is set to false when the dyn_gen for the timeslot is changed Only used when the PropagatorComputer uses diagonalisation dyn_gen_eigenvectors : List of array[drift_dyn_gen.shape] Eigenvectors of the dynamics generators Used for calculating the propagators and their gradients Only used when the PropagatorComputer uses diagonalisation prop_eigen : List of array[drift_dyn_gen.shape] Propagator in diagonalised basis of the combined dynamics generator Used for calculating the propagators and their gradients Only used when the PropagatorComputer uses diagonalisation dyn_gen_factormatrix : List of array[drift_dyn_gen.shape] Matrix of scaling factors calculated duing the decomposition Used for calculating the propagator gradients Only used when the PropagatorComputer uses diagonalisation 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 """ def __init__(self, optimconfig): self.config = optimconfig self.reset() def reset(self): # Main functional attributes self.evo_time = 0 self.num_tslots = 0 self.tau = None 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.dyn_gen = None self.prop = None self.prop_grad = None self.evo_init2t = None self.evo_t2end = None self.evo_t2targ = None # Atrributes used in diagonalisation self.decomp_curr = None self.prop_eigen = None self.dyn_gen_eigenvectors = 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.set_log_level(self.config.log_level) # Internal flags self._dyn_gen_mapped = False self._ctrls_initialized = False # Create the computing objects self._create_computers() self.clear()
[docs] def set_log_level(self, lvl): """ Set the log_level attribute and set the level of the logger that is call logger.setLevel(lvl) """ self.log_level = lvl logger.setLevel(lvl)
def _create_computers(self): """ Create the default timeslot, fidelity and propagator computers """ # The time slot computer. By default it is set to UpdateAll # can be set to DynUpdate in the configuration # (see class file for details) if self.config.amp_update_mode == 'DYNAMIC': self.tslot_computer = tslotcomp.TSlotCompDynUpdate(self) else: self.tslot_computer = tslotcomp.TSlotCompUpdateAll(self) self.prop_computer = propcomp.PropCompFrechet(self) self.fid_computer = fidcomp.FidCompTraceDiff(self) def clear(self): self.ctrl_amps = None self.evo_current = False if self.fid_computer is not None: self.fid_computer.clear()
[docs] def init_time_slots(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.tau is None: self.tau = np.ones(self.num_tslots, dtype='f') * \ self.evo_time/self.num_tslots 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]
def _init_lists(self): """ Create the container lists / arrays for the: dynamics generations, propagators, and evolutions etc Set the time slices and cumulative time """ # Create containers for control Hamiltonian etc shp = self.drift_dyn_gen.shape # set H to be just empty float arrays with the shape of H self.dyn_gen = [np.empty(shp, dtype=complex) for x in range(self.num_tslots)] # the exponetiation of H. Just empty float arrays with the shape of H self.prop = [np.empty(shp, dtype=complex) for x in range(self.num_tslots)] if self.prop_computer.grad_exact: self.prop_grad = np.empty([self.num_tslots, self.get_num_ctrls()], dtype=np.ndarray) # Time evolution operator (forward propagation) self.evo_init2t = [np.empty(shp, dtype=complex) for x in range(self.num_tslots + 1)] self.evo_init2t[0] = self.initial if self.fid_computer.uses_evo_t2end: # Time evolution operator (onward propagation) self.evo_t2end = [np.empty(shp, dtype=complex) for x in range(self.num_tslots)] if self.fid_computer.uses_evo_t2targ: # Onward propagation overlap with inverse target self.evo_t2targ = [np.empty(shp, dtype=complex) for x in range(self.num_tslots + 1)] self.evo_t2targ[-1] = self.get_owd_evo_target() if isinstance(self.prop_computer, propcomp.PropCompDiag): self._create_decomp_lists() 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 """ shp = self.drift_dyn_gen.shape n_ts = self.num_tslots self.decomp_curr = [False for x in range(n_ts)] self.prop_eigen = \ [np.empty(shp[0], dtype=complex) for x in range(n_ts)] self.dyn_gen_eigenvectors = \ [np.empty(shp, dtype=complex) for x in range(n_ts)] self.dyn_gen_factormatrix = \ [np.empty(shp, dtype=complex) for x in range(n_ts)] def _check_test_out_files(self): cfg = self.config if cfg.any_test_files(): if not cfg.check_create_test_out_dir(): cfg.reset_test_out_files() else: if self.stats is None: logger.warn("Cannot output test files when stats" " attribute is not set.") self.config.test_out_amps = False self.config.test_out_prop = False self.config.test_out_prop_grad = False self.config.test_out_evo = False
[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 """ self._check_test_out_files() 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.FideliyComputer): raise errors.UsageError( "No fid_computer (Fidelity computer)" " set. A default should be assigned by the Dynamics subclass") self.ctrl_amps = None # Note this call is made just to initialise the num_ctrls attrib self.get_num_ctrls() if init_tslots: self.init_time_slots() self._init_lists() 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 isinstance(times, string_types): 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)) if not self.tslot_computer.compare_amps(new_amps): if self.config.test_out_amps: fname = "amps_{}_{}_{}_call{}{}".format( self.id_text, self.prop_computer.id_text, self.fid_computer.id_text, self.stats.num_ctrl_amp_updates, self.config.test_out_f_ext) fpath = os.path.join(self.config.test_out_dir, fname) self.save_amps(fpath, verbose=True)
[docs] def flag_system_changed(self): """ Flag eveolution, 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 not isinstance(self.drift_dyn_gen, np.ndarray): raise TypeError("Cannot get drift dimension, " "as drift not set (correctly).") return self.drift_dyn_gen.shape[0]
[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 """ self.num_ctrls = len(self.ctrl_dyn_gen) return self.num_ctrls
[docs] def get_owd_evo_target(self): """ Get the inverse of the target. Used for calculating the 'backward' evolution """ return la.inv(self.target)
[docs] def combine_dyn_gen(self, k): """ Computes the dynamics generator for a given timeslot The is the combined Hamiltion for unitary systems """ dg = np.asarray(self.drift_dyn_gen) for j in range(self.get_num_ctrls()): dg = dg + self.ctrl_amps[k, j]*np.asarray(self.ctrl_dyn_gen[j]) return 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 """ raise errors.UsageError("Not implemented in the baseclass." " Choose a subclass")
[docs] def get_ctrl_dyn_gen(self, j): """ Get the dynamics generator for the control Not implemented in the base class. Choose a subclass """ raise errors.UsageError("Not implemented in the baseclass." " Choose a subclass")
[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
[docs] 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)
[docs] 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")
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, Limbladian """ def reset(self): Dynamics.reset(self) self.id_text = 'GEN_MAT' def get_dyn_gen(self, k): """ Get the combined dynamics generator for the timeslot This base class method simply returns dyn_gen[k] other subclass methods will include some factor """ return self.dyn_gen[k] def get_ctrl_dyn_gen(self, j): """ Get the dynamics generator for the control This base class method simply returns ctrl_dyn_gen[j] other subclass methods will include some factor """ return self.ctrl_dyn_gen[j]
[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 def _create_computers(self): """ Create the default timeslot, fidelity and propagator computers """ # The time slot computer. By default it is set to _UpdateAll # can be set to _DynUpdate in the configuration # (see class file for details) if self.config.amp_update_mode == 'DYNAMIC': self.tslot_computer = tslotcomp.TSlotCompDynUpdate(self) else: self.tslot_computer = tslotcomp.TSlotCompUpdateAll(self) # set the default fidelity computer self.fid_computer = fidcomp.FidCompUnitary(self) # set the default propagator computer self.prop_computer = propcomp.PropCompDiag(self) 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
[docs] def get_dyn_gen(self, k): """ Get the combined dynamics generator for the timeslot including the -i factor """ return -1j*self.dyn_gen[k]
[docs] def get_ctrl_dyn_gen(self, j): """ Get the dynamics generator for the control including the -i factor """ return -1j*self.ctrl_dyn_gen[j]
def get_num_ctrls(self): if not self._dyn_gen_mapped: self._map_dyn_gen_to_ham() return Dynamics.get_num_ctrls(self) def get_owd_evo_target(self): return self.target.conj().T
[docs] 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 """ H = self.H[k] # assuming H is an nxn matrix, find n n = H.shape[0] # returns row vector of eigen values, # columns with the eigenvectors eig_val, eig_vec = np.linalg.eig(H) # 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 eigenvals and eigenvectors for use by other functions, e.g. # gradient_exact self.decomp_curr[k] = True self.prop_eigen[k] = prop_eig self.dyn_gen_eigenvectors[k] = eig_vec self.dyn_gen_factormatrix[k] = factors
[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.grad_exact = True def _create_computers(self): """ Create the default timeslot, fidelity and propagator computers """ # The time slot computer. By default it is set to _UpdateAll # can be set to _DynUpdate in the configuration # (see class file for details) if self.config.amp_update_mode == 'DYNAMIC': self.tslot_computer = tslotcomp.TSlotCompDynUpdate(self) else: self.tslot_computer = tslotcomp.TSlotCompUpdateAll(self) self.prop_computer = propcomp.PropCompFrechet(self) self.fid_computer = fidcomp.FidCompTraceDiff(self) def get_omega(self): if self.omega is None: n = self.drift_dyn_gen.shape[0] // 2 self.omega = sympl.calc_omega(n) return self.omega
[docs] def get_dyn_gen(self, k): """ Get the combined dynamics generator for the timeslot multiplied by omega """ o = self.get_omega() return self.dyn_gen[k].dot(o)
[docs] def get_ctrl_dyn_gen(self, j): """ Get the dynamics generator for the control multiplied by omega """ o = self.get_omega() return self.ctrl_dyn_gen[j].dot(o)