qutip_qtrl.fidcomp

Fidelity Computer

These classes calculate the fidelity error - function to be minimised and fidelity error gradient, which is used to direct the optimisation

They may calculate the fidelity as an intermediary step, as in some case e.g. unitary dynamics, this is more efficient

The idea is that different methods for computing the fidelity can be tried and compared using simple configuration switches.

Note the methods in these classes were inspired by: DYNAMO - Dynamic Framework for Quantum Optimal Control See Machnes et.al., arXiv.1011.4874 The unitary dynamics fidelity is taken directly frm DYNAMO The other fidelity measures are extensions, and the sources are given in the class descriptions.

Classes

FidCompTraceDiff(dynamics[, params])	Computes fidelity error and gradient for general system dynamics by calculating the the fidelity error as the trace of the overlap of the difference between the target and evolution resulting from the pulses with the transpose of the same.
FidCompTraceDiffApprox(dynamics[, params])	As FidCompTraceDiff, except uses the finite difference method to compute approximate gradients
FidCompUnitary(dynamics[, params])	Computes fidelity error and gradient assuming unitary dynamics, e.g.
FidelityComputer(dynamics[, params])	Base class for all Fidelity Computers.

class qutip_qtrl.fidcomp.FidCompTraceDiff(dynamics, params=None)

Computes fidelity error and gradient for general system dynamics by calculating the the fidelity error as the trace of the overlap of the difference between the target and evolution resulting from the pulses with the transpose of the same. This should provide a distance measure for dynamics described by matrices Note the gradient calculation is taken from: ‘Robust quantum gates for open systems via optimal control: Markovian versus non-Markovian dynamics’ Frederik F Floether, Pierre de Fouquieres, and Sophie G Schirmer

Attributes:

scale_factorfloat

The fidelity error calculated is of some arbitary scale. This factor can be used to scale the fidelity error such that it may represent some physical measure If None is given then it is caculated as 1/2N, where N is the dimension of the drift, when the Dynamics are initialised.

compute_fid_err_grad()

Calculate exact gradient of the fidelity error function wrt to each timeslot control amplitudes. Uses the trace difference norm fidelity These are returned as a (nTimeslots x n_ctrls) array

get_fid_err()

Gets the absolute error in the fidelity

get_fid_err_gradient()

Returns the normalised gradient of the fidelity error in a (nTimeslots x n_ctrls) array The gradients are cached in case they are requested mutliple times between control updates (although this is not typically found to happen)

init_comp()

initialises the computer based on the configuration of the Dynamics Calculates the scale_factor is not already set

reset()

reset any configuration data and clear any temporarily held status data

class qutip_qtrl.fidcomp.FidCompTraceDiffApprox(dynamics, params=None)

As FidCompTraceDiff, except uses the finite difference method to compute approximate gradients

Attributes:

epsilonfloat

control amplitude offset to use when approximating the gradient wrt a timeslot control amplitude

compute_fid_err_grad()

Calculates gradient of function wrt to each timeslot control amplitudes. Note these gradients are not normalised They are calulated These are returned as a (nTimeslots x n_ctrls) array

reset()

reset any configuration data and clear any temporarily held status data

class qutip_qtrl.fidcomp.FidCompUnitary(dynamics, params=None)

Computes fidelity error and gradient assuming unitary dynamics, e.g. closed qubit systems Note fidelity and gradient calculations were taken from DYNAMO (see file header)

Attributes:

phase_optionstring

determines how global phase is treated in fidelity calculations:

PSU - global phase ignored SU - global phase included

fidelity_prenormcomplex

Last computed value of the fidelity before it is normalised It is stored to use in the gradient normalisation calculation

fidelity_prenorm_currentboolean

flag to specify whether fidelity_prenorm are based on the current amplitude values. Set False when amplitudes change

clear()

clear any temporarily held status data

compute_fid_grad()

Calculates exact gradient of function wrt to each timeslot control amplitudes. Note these gradients are not normalised These are returned as a (nTimeslots x n_ctrls) array

flag_system_changed()

Flag fidelity and gradients as needing recalculation

get_fid_err()

Gets the absolute error in the fidelity

get_fid_err_gradient()

Returns the normalised gradient of the fidelity error in a (nTimeslots x n_ctrls) array The gradients are cached in case they are requested mutliple times between control updates (although this is not typically found to happen)

get_fidelity()

Gets the appropriately normalised fidelity value The normalisation is determined by the fid_norm_func pointer which should be set in the config

get_fidelity_prenorm()

Gets the current fidelity value prior to normalisation Note the gradient function uses this value The value is cached, because it is used in the gradient calculation

init_comp()

Check configuration and initialise the normalisation

init_normalization()

Calc norm of <Ufinal | Ufinal> to scale subsequent norms When considering unitary time evolution operators, this basically results in calculating the trace of the identity matrix and is hence equal to the size of the target matrix There may be situations where this is not the case, and hence it is not assumed to be so. The normalisation function called should be set to either the PSU - global phase ignored SU - global phase respected

normalize_gradient_PSU(grad)

Normalise the gradient matrix passed as grad This PSU version is independent of global phase

normalize_gradient_SU(grad)

Normalise the gradient matrix passed as grad This SU version respects global phase

reset()

reset any configuration data and clear any temporarily held status data

set_phase_option(phase_option=None)

Deprecated - use phase_option Phase options are SU - global phase important PSU - global phase is not important

class qutip_qtrl.fidcomp.FidelityComputer(dynamics, params=None)

Base class for all Fidelity Computers. This cannot be used directly. See subclass descriptions and choose one appropriate for the application Note: this must be instantiated with a Dynamics object, that is the container for the data that the methods operate on

Attributes:

log_levelinteger

level of messaging output from the logger. Options are attributes of qutip_qtrl.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

dimensional_normfloat

Normalisation constant

fid_norm_funcfunction

Used to normalise the fidelity See SU and PSU options for the unitary dynamics

grad_norm_funcfunction

Used to normalise the fidelity gradient See SU and PSU options for the unitary dynamics

uses_onwd_evoboolean

flag to specify whether the onwd_evo evolution operator (see Dynamics) is used by the FidelityComputer

uses_onto_evoboolean

flag to specify whether the onto_evo evolution operator

(see Dynamics) is used by the FidelityComputer

fid_errfloat

Last computed value of the fidelity error

fidelityfloat

Last computed value of the normalised fidelity

fidelity_currentboolean

flag to specify whether the fidelity / fid_err are based on the current amplitude values. Set False when amplitudes change

fid_err_grad: array[num_tslot, num_ctrls] of float

Last computed values for the fidelity error gradients wrt the control in the timeslot

grad_normfloat

Last computed value for the norm of the fidelity error gradients (sqrt of the sum of the squares)

fid_err_grad_currentboolean

flag to specify whether the fidelity / fid_err are based on the current amplitude values. Set False when amplitudes change

apply_params(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.

clear()

clear any temporarily held status data

flag_system_changed()

Flag fidelity and gradients as needing recalculation

get_fid_err()

returns the absolute distance from the maximum achievable fidelity

get_fid_err_gradient()

Returns the normalised gradient of the fidelity error in a (nTimeslots x n_ctrls) array wrt the timeslot control amplitude

init_comp()

initialises the computer based on the configuration of the Dynamics

reset()

reset any configuration data and clear any temporarily held status data