# Classes¶

## Qobj¶

class Qobj(inpt=None, dims=[[], []], shape=[], type=None, isherm=None, copy=True, fast=False, superrep=None)[source]

A class for representing quantum objects, such as quantum operators and states.

The Qobj class is the QuTiP representation of quantum operators and state vectors. This class also implements math operations +,-,* between Qobj instances (and / by a C-number), as well as a collection of common operator/state operations. The Qobj constructor optionally takes a dimension list and/or shape list as arguments.

Parameters: inpt (array_like) – Data for vector/matrix representation of the quantum object. dims (list) – Dimensions of object used for tensor products. shape (list) – Shape of underlying data structure (matrix shape). copy (bool) – Flag specifying whether Qobj should get a copy of the input data, or use the original. fast (bool) – Flag for fast qobj creation when running ode solvers. This parameter is used internally only.
data

array_like – Sparse matrix characterizing the quantum object.

dims

list – List of dimensions keeping track of the tensor structure.

shape

list – Shape of the underlying data array.

type

str – Type of quantum object: ‘bra’, ‘ket’, ‘oper’, ‘operator-ket’, ‘operator-bra’, or ‘super’.

superrep

str – Representation used if type is ‘super’. One of ‘super’ (Liouville form) or ‘choi’ (Choi matrix with tr = dimension).

isherm

bool – Indicates if quantum object represents Hermitian operator.

iscp

bool – Indicates if the quantum object represents a map, and if that map is completely positive (CP).

ishp

bool – Indicates if the quantum object represents a map, and if that map is hermicity preserving (HP).

istp

bool – Indicates if the quantum object represents a map, and if that map is trace preserving (TP).

iscptp

bool – Indicates if the quantum object represents a map that is completely positive and trace preserving (CPTP).

isket

bool – Indicates if the quantum object represents a ket.

isbra

bool – Indicates if the quantum object represents a bra.

isoper

bool – Indicates if the quantum object represents an operator.

issuper

bool – Indicates if the quantum object represents a superoperator.

isoperket

bool – Indicates if the quantum object represents an operator in column vector form.

isoperbra

bool – Indicates if the quantum object represents an operator in row vector form.

conj()

Conjugate of quantum object.

cosm()

Cosine of quantum object.

dag()

Adjoint (dagger) of quantum object.

dnorm()

Diamond norm of quantum operator.

dual_chan()

Dual channel of quantum object representing a CP map.

eigenenergies(sparse=False, sort='low', eigvals=0, tol=0, maxiter=100000)

Returns eigenenergies (eigenvalues) of a quantum object.

eigenstates(sparse=False, sort='low', eigvals=0, tol=0, maxiter=100000)

Returns eigenenergies and eigenstates of quantum object.

expm()

Matrix exponential of quantum object.

full()

Returns dense array of quantum object data attribute.

groundstate(sparse=False, tol=0, maxiter=100000)

Returns eigenvalue and eigenket for the groundstate of a quantum object.

matrix_element(bra, ket)

Returns the matrix element of operator between bra and ket vectors.

norm(norm='tr', sparse=False, tol=0, maxiter=100000)

Returns norm of a ket or an operator.

permute(order)

Returns composite qobj with indices reordered.

ptrace(sel)

Returns quantum object for selected dimensions after performing partial trace.

sinm()

Sine of quantum object.

sqrtm()

Matrix square root of quantum object.

tidyup(atol=1e-12)

Removes small elements from quantum object.

tr()

Trace of quantum object.

trans()

Transpose of quantum object.

transform(inpt, inverse=False)

Performs a basis transformation defined by inpt matrix.

trunc_neg(method='clip')

Removes negative eigenvalues and returns a new Qobj that is a valid density operator.

unit(norm='tr', sparse=False, tol=0, maxiter=100000)

Returns normalized quantum object.

check_herm()[source]

Check if the quantum object is hermitian.

Returns: isherm – Returns the new value of isherm property. bool
conj()[source]

Conjugate operator of quantum object.

cosm()[source]

Cosine of a quantum operator.

Operator must be square.

Returns: oper – Matrix cosine of operator. qobj TypeError – Quantum object is not square.

Notes

Uses the Q.expm() method.

dag()[source]

Adjoint operator of quantum object.

diag()[source]

Diagonal elements of quantum object.

Returns: diags – Returns array of real values if operators is Hermitian, otherwise complex values are returned. array
dnorm(B=None)[source]

Calculates the diamond norm, or the diamond distance to another operator.

Parameters: B (Qobj or None) – If B is not None, the diamond distance d(A, B) = dnorm(A - B) between this operator and B is returned instead of the diamond norm. d – Either the diamond norm of this operator, or the diamond distance from this operator to B. float
dual_chan()[source]

Dual channel of quantum object representing a completely positive map.

eigenenergies(sparse=False, sort='low', eigvals=0, tol=0, maxiter=100000)[source]

Eigenenergies of a quantum object.

Eigenenergies (eigenvalues) are defined for operators or superoperators only.

Parameters: sparse (bool) – Use sparse Eigensolver sort (str) – Sort eigenvalues ‘low’ to high, or ‘high’ to low. eigvals (int) – Number of requested eigenvalues. Default is all eigenvalues. tol (float) – Tolerance used by sparse Eigensolver (0=machine precision). The sparse solver may not converge if the tolerance is set too low. maxiter (int) – Maximum number of iterations performed by sparse solver (if used). eigvals – Array of eigenvalues for operator. array

Notes

The sparse eigensolver is much slower than the dense version. Use sparse only if memory requirements demand it.

eigenstates(sparse=False, sort='low', eigvals=0, tol=0, maxiter=100000)[source]

Eigenstates and eigenenergies.

Eigenstates and eigenenergies are defined for operators and superoperators only.

Parameters: sparse (bool) – Use sparse Eigensolver sort (str) – Sort eigenvalues (and vectors) ‘low’ to high, or ‘high’ to low. eigvals (int) – Number of requested eigenvalues. Default is all eigenvalues. tol (float) – Tolerance used by sparse Eigensolver (0 = machine precision). The sparse solver may not converge if the tolerance is set too low. maxiter (int) – Maximum number of iterations performed by sparse solver (if used). eigvals (array) – Array of eigenvalues for operator. eigvecs (array) – Array of quantum operators representing the oprator eigenkets. Order of eigenkets is determined by order of eigenvalues.

Notes

The sparse eigensolver is much slower than the dense version. Use sparse only if memory requirements demand it.

eliminate_states(states_inds, normalize=False)[source]

Creates a new quantum object with states in state_inds eliminated.

Parameters: states_inds (list of integer) – The states that should be removed. normalize (True / False) – Weather or not the new Qobj instance should be normalized (default is False). For Qobjs that represents density matrices or state vectors normalized should probably be set to True, but for Qobjs that represents operators in for example an Hamiltonian, normalize should be False. q – A new instance of qutip.Qobj that contains only the states corresponding to indices that are not in state_inds. Qobj

Notes

Experimental.

static evaluate(qobj_list, t, args)[source]

Evaluate a time-dependent quantum object in list format. For example,

qobj_list = [H0, [H1, func_t]]

is evaluated to

Qobj(t) = H0 + H1 * func_t(t, args)

and

qobj_list = [H0, [H1, ‘sin(w * t)’]]

is evaluated to

Qobj(t) = H0 + H1 * sin(args[‘w’] * t)
Parameters: qobj_list (list) – A nested list of Qobj instances and corresponding time-dependent coefficients. t (float) – The time for which to evaluate the time-dependent Qobj instance. args (dictionary) – A dictionary with parameter values required to evaluate the time-dependent Qobj intance. output – A Qobj instance that represents the value of qobj_list at time t. Qobj
expm(method='dense')[source]

Matrix exponential of quantum operator.

Input operator must be square.

Parameters: method (str {'dense', 'sparse'}) – Use set method to use to calculate the matrix exponentiation. The available choices includes ‘dense’ and ‘sparse’. Since the exponential of a matrix is nearly always dense, method=’dense’ is set as default.s oper – Exponentiated quantum operator. qobj TypeError – Quantum operator is not square.
extract_states(states_inds, normalize=False)[source]

Qobj with states in state_inds only.

Parameters: states_inds (list of integer) – The states that should be kept. normalize (True / False) – Weather or not the new Qobj instance should be normalized (default is False). For Qobjs that represents density matrices or state vectors normalized should probably be set to True, but for Qobjs that represents operators in for example an Hamiltonian, normalize should be False. q – A new instance of qutip.Qobj that contains only the states corresponding to the indices in state_inds. Qobj

Notes

Experimental.

full(squeeze=False)[source]

Dense array from quantum object.

Returns: data – Array of complex data from quantum objects data attribute. array
groundstate(sparse=False, tol=0, maxiter=100000, safe=True)[source]

Ground state Eigenvalue and Eigenvector.

Defined for quantum operators or superoperators only.

Parameters: sparse (bool) – Use sparse Eigensolver tol (float) – Tolerance used by sparse Eigensolver (0 = machine precision). The sparse solver may not converge if the tolerance is set too low. maxiter (int) – Maximum number of iterations performed by sparse solver (if used). safe (bool (default=True)) – Check for degenerate ground state eigval (float) – Eigenvalue for the ground state of quantum operator. eigvec (qobj) – Eigenket for the ground state of quantum operator.

Notes

The sparse eigensolver is much slower than the dense version. Use sparse only if memory requirements demand it.

matrix_element(bra, ket)[source]

Calculates a matrix element.

Gives the matrix element for the quantum object sandwiched between a bra and ket vector.

Parameters: bra (qobj) – Quantum object of type ‘bra’. ket (qobj) – Quantum object of type ‘ket’. elem – Complex valued matrix element. complex TypeError – Can only calculate matrix elements between a bra and ket quantum object.
norm(norm=None, sparse=False, tol=0, maxiter=100000)[source]

Norm of a quantum object.

Default norm is L2-norm for kets and trace-norm for operators. Other ket and operator norms may be specified using the norm and argument.

Parameters: norm (str) – Which norm to use for ket/bra vectors: L2 ‘l2’, max norm ‘max’, or for operators: trace ‘tr’, Frobius ‘fro’, one ‘one’, or max ‘max’. sparse (bool) – Use sparse eigenvalue solver for trace norm. Other norms are not affected by this parameter. tol (float) – Tolerance for sparse solver (if used) for trace norm. The sparse solver may not converge if the tolerance is set too low. maxiter (int) – Maximum number of iterations performed by sparse solver (if used) for trace norm. norm – The requested norm of the operator or state quantum object. float

Notes

The sparse eigensolver is much slower than the dense version. Use sparse only if memory requirements demand it.

overlap(state)[source]

Overlap between two state vectors.

Gives the overlap (scalar product) for the quantum object and state state vector.

Parameters: state (qobj) – Quantum object for a state vector of type ‘ket’ or ‘bra’. overlap – Complex valued overlap. complex TypeError – Can only calculate overlap between a bra and ket quantum objects.
permute(order)[source]

Permutes a composite quantum object.

Parameters: order (list/array) – List specifying new tensor order. P – Permuted quantum object. qobj
ptrace(sel)[source]

Partial trace of the quantum object.

Parameters: sel (int/list) – An int or list of components to keep after partial trace. oper – Quantum object representing partial trace with selected components remaining. qobj

Notes

This function is identical to the qutip.qobj.ptrace function that has been deprecated.

sinm()[source]

Sine of a quantum operator.

Operator must be square.

Returns: oper – Matrix sine of operator. qobj TypeError – Quantum object is not square.

Notes

Uses the Q.expm() method.

sqrtm(sparse=False, tol=0, maxiter=100000)[source]

Sqrt of a quantum operator.

Operator must be square.

Parameters: sparse (bool) – Use sparse eigenvalue/vector solver. tol (float) – Tolerance used by sparse solver (0 = machine precision). maxiter (int) – Maximum number of iterations used by sparse solver. oper – Matrix square root of operator. qobj TypeError – Quantum object is not square.

Notes

The sparse eigensolver is much slower than the dense version. Use sparse only if memory requirements demand it.

tidyup(atol=None)[source]

Removes small elements from the quantum object.

Parameters: atol (float) – Absolute tolerance used by tidyup. Default is set via qutip global settings parameters. oper – Quantum object with small elements removed. qobj
tr()[source]

Trace of a quantum object.

Returns: trace – Returns real if operator is Hermitian, returns complex otherwise. float
trans()[source]

Transposed operator.

Returns: oper – Transpose of input operator. qobj
transform(inpt, inverse=False, sparse=True)[source]

Basis transform defined by input array.

Input array can be a matrix defining the transformation, or a list of kets that defines the new basis.

Parameters: inpt (array_like) – A matrix or list of kets defining the transformation. inverse (bool) – Whether to return inverse transformation. sparse (bool) – Use sparse matrices when possible. Can be slower. oper – Operator in new basis. qobj

Notes

This function is still in development.

trunc_neg(method='clip')[source]

Truncates negative eigenvalues and renormalizes.

Returns a new Qobj by removing the negative eigenvalues of this instance, then renormalizing to obtain a valid density operator.

Parameters: method (str) – Algorithm to use to remove negative eigenvalues. “clip” simply discards negative eigenvalues, then renormalizes. “sgs” uses the SGS algorithm (doi:10/bb76) to find the positive operator that is nearest in the Shatten 2-norm. oper – A valid density operator. qobj
unit(norm=None, sparse=False, tol=0, maxiter=100000)[source]

Operator or state normalized to unity.

Uses norm from Qobj.norm().

Parameters: norm (str) – Requested norm for states / operators. sparse (bool) – Use sparse eigensolver for trace norm. Does not affect other norms. tol (float) – Tolerance used by sparse eigensolver. maxiter (int) – Number of maximum iterations performed by sparse eigensolver. oper – Normalized quantum object. qobj

## eseries¶

class eseries(q=array([], dtype=object), s=array([], dtype=float64))[source]

Class representation of an exponential-series expansion of time-dependent quantum objects.

ampl

ndarray – Array of amplitudes for exponential series.

rates

ndarray – Array of rates for exponential series.

dims

list – Dimensions of exponential series components

shape

list – Shape corresponding to exponential series components

value(tlist)

Evaluate an exponential series at the times listed in tlist

spec(wlist)

Evaluate the spectrum of an exponential series at frequencies in wlist.

tidyup()

Returns a tidier version of the exponential series

spec(wlist)[source]

Evaluate the spectrum of an exponential series at frequencies in wlist.

Parameters: wlist (array_like) – Array/list of frequenies. val_list – Values of exponential series at frequencies in wlist. ndarray
tidyup(*args)[source]

Returns a tidier version of exponential series.

value(tlist)[source]

Evaluates an exponential series at the times listed in tlist.

Parameters: tlist (ndarray) – Times at which to evaluate exponential series. val_list – Values of exponential at times in tlist. ndarray

## Bloch sphere¶

class Bloch(fig=None, axes=None, view=None, figsize=None, background=False)[source]

Class for plotting data on the Bloch sphere. Valid data can be either points, vectors, or qobj objects.

axes

instance {None} – User supplied Matplotlib axes for Bloch sphere animation.

fig

instance {None} – User supplied Matplotlib Figure instance for plotting Bloch sphere.

font_color

str {‘black’} – Color of font used for Bloch sphere labels.

font_size

int {20} – Size of font used for Bloch sphere labels.

frame_alpha

float {0.1} – Sets transparency of Bloch sphere frame.

frame_color

str {‘gray’} – Color of sphere wireframe.

frame_width

int {1} – Width of wireframe.

point_color

list {[“b”,”r”,”g”,”#CC6600”]} – List of colors for Bloch sphere point markers to cycle through. i.e. By default, points 0 and 4 will both be blue (‘b’).

point_marker

list {[“o”,”s”,”d”,”^”]} – List of point marker shapes to cycle through.

point_size

list {[25,32,35,45]} – List of point marker sizes. Note, not all point markers look the same size when plotted!

sphere_alpha

float {0.2} – Transparency of Bloch sphere itself.

sphere_color

str {‘#FFDDDD’} – Color of Bloch sphere.

figsize

list {[7,7]} – Figure size of Bloch sphere plot. Best to have both numbers the same; otherwise you will have a Bloch sphere that looks like a football.

vector_color

list {[“g”,”#CC6600”,”b”,”r”]} – List of vector colors to cycle through.

vector_width

int {5} – Width of displayed vectors.

vector_style

str {‘-|>’, ‘simple’, ‘fancy’, ‘’} – Vector arrowhead style (from matplotlib’s arrow style).

vector_mutation

int {20} – Width of vectors arrowhead.

view

list {[-60,30]} – Azimuthal and Elevation viewing angles.

xlabel

list {[“$x$”,”“]} – List of strings corresponding to +x and -x axes labels, respectively.

xlpos

list {[1.1,-1.1]} – Positions of +x and -x labels respectively.

ylabel

list {[“$y$”,”“]} – List of strings corresponding to +y and -y axes labels, respectively.

ylpos

list {[1.2,-1.2]} – Positions of +y and -y labels respectively.

zlabel

list {[r’$left|0right>$’,r’$left|1right>$’]} – List of strings corresponding to +z and -z axes labels, respectively.

zlpos

list {[1.2,-1.2]} – Positions of +z and -z labels respectively.

add_annotation(state_or_vector, text, **kwargs)[source]

Add a text or LaTeX annotation to Bloch sphere, parametrized by a qubit state or a vector.

Parameters: state_or_vector (Qobj/array/list/tuple) – Position for the annotaion. Qobj of a qubit or a vector of 3 elements. text (str/unicode) – Annotation text. You can use LaTeX, but remember to use raw string e.g. r”$langle x rangle$” or escape backslashes e.g. “$\langle x \rangle$”. **kwargs – Options as for mplot3d.axes3d.text, including: fontsize, color, horizontalalignment, verticalalignment.
add_points(points, meth='s')[source]

Add a list of data points to bloch sphere.

Parameters: points (array/list) – Collection of data points. meth (str {'s', 'm', 'l'}) – Type of points to plot, use ‘m’ for multicolored, ‘l’ for points connected with a line.
add_states(state, kind='vector')[source]

Add a state vector Qobj to Bloch sphere.

Parameters: state (qobj) – Input state vector. kind (str {'vector','point'}) – Type of object to plot.
add_vectors(vectors)[source]

Add a list of vectors to Bloch sphere.

Parameters: vectors (array_like) – Array with vectors of unit length or smaller.
clear()[source]

Resets Bloch sphere data sets to empty.

make_sphere()[source]

Plots Bloch sphere and data sets.

render(fig=None, axes=None)[source]

Render the Bloch sphere and its data sets in on given figure and axes.

save(name=None, format='png', dirc=None)[source]

Saves Bloch sphere to file of type format in directory dirc.

Parameters: name (str) – Name of saved image. Must include path and format as well. i.e. ‘/Users/Paul/Desktop/bloch.png’ This overrides the ‘format’ and ‘dirc’ arguments. format (str) – Format of output image. dirc (str) – Directory for output images. Defaults to current working directory. File containing plot of Bloch sphere.
set_label_convention(convention)[source]

Set x, y and z labels according to one of conventions.

Parameters: convention (string) – One of the following: “original” “xyz” “sx sy sz” “01” “polarization jones” “polarization jones letters” see also: http://en.wikipedia.org/wiki/Jones_calculus “polarization stokes” see also: http://en.wikipedia.org/wiki/Stokes_parameters
show()[source]

Display Bloch sphere and corresponding data sets.

vector_mutation = None

Sets the width of the vectors arrowhead

vector_style = None

Style of Bloch vectors, default = ‘-|>’ (or ‘simple’)

vector_width = None

Width of Bloch vectors, default = 5

class Bloch3d(fig=None)[source]

Class for plotting data on a 3D Bloch sphere using mayavi. Valid data can be either points, vectors, or qobj objects corresponding to state vectors or density matrices. for a two-state system (or subsystem).

fig

instance {None} – User supplied Matplotlib Figure instance for plotting Bloch sphere.

font_color

str {‘black’} – Color of font used for Bloch sphere labels.

font_scale

float {0.08} – Scale for font used for Bloch sphere labels.

frame

bool {True} – Draw frame for Bloch sphere

frame_alpha

float {0.05} – Sets transparency of Bloch sphere frame.

frame_color

str {‘gray’} – Color of sphere wireframe.

frame_num

int {8} – Number of frame elements to draw.

frame_radius

floats {0.005} – Width of wireframe.

point_color

list {[‘r’, ‘g’, ‘b’, ‘y’]} – List of colors for Bloch sphere point markers to cycle through. i.e. By default, points 0 and 4 will both be blue (‘r’).

point_mode

string {‘sphere’,’cone’,’cube’,’cylinder’,’point’} – Point marker shapes.

point_size

float {0.075} – Size of points on Bloch sphere.

sphere_alpha

float {0.1} – Transparency of Bloch sphere itself.

sphere_color

str {‘#808080’} – Color of Bloch sphere.

size

list {[500,500]} – Size of Bloch sphere plot in pixels. Best to have both numbers the same otherwise you will have a Bloch sphere that looks like a football.

vector_color

list {[‘r’, ‘g’, ‘b’, ‘y’]} – List of vector colors to cycle through.

vector_width

int {3} – Width of displayed vectors.

view

list {[45,65]} – Azimuthal and Elevation viewing angles.

xlabel

list {[‘|x>’, ‘’]} – List of strings corresponding to +x and -x axes labels, respectively.

xlpos

list {[1.07,-1.07]} – Positions of +x and -x labels respectively.

ylabel

list {[‘|y>’, ‘’]} – List of strings corresponding to +y and -y axes labels, respectively.

ylpos

list {[1.07,-1.07]} – Positions of +y and -y labels respectively.

zlabel

list {[‘|0>’, ‘|1>’]} – List of strings corresponding to +z and -z axes labels, respectively.

zlpos

list {[1.07,-1.07]} – Positions of +z and -z labels respectively.

Notes

The use of mayavi for 3D rendering of the Bloch sphere comes with a few limitations: I) You can not embed a Bloch3d figure into a matplotlib window. II) The use of LaTex is not supported by the mayavi rendering engine. Therefore all labels must be defined using standard text. Of course you can post-process the generated figures later to add LaTeX using other software if needed.

add_points(points, meth='s')[source]

Add a list of data points to bloch sphere.

Parameters: points (array/list) – Collection of data points. meth (str {'s','m'}) – Type of points to plot, use ‘m’ for multicolored.
add_states(state, kind='vector')[source]

Add a state vector Qobj to Bloch sphere.

Parameters: state (qobj) – Input state vector. kind (str {'vector','point'}) – Type of object to plot.
add_vectors(vectors)[source]

Add a list of vectors to Bloch sphere.

Parameters: vectors (array/list) – Array with vectors of unit length or smaller.
clear()[source]

Resets the Bloch sphere data sets to empty.

make_sphere()[source]

Plots Bloch sphere and data sets.

plot_points()[source]

Plots points on the Bloch sphere.

plot_vectors()[source]

Plots vectors on the Bloch sphere.

save(name=None, format='png', dirc=None)[source]

Saves Bloch sphere to file of type format in directory dirc.

Parameters: name (str) – Name of saved image. Must include path and format as well. i.e. ‘/Users/Paul/Desktop/bloch.png’ This overrides the ‘format’ and ‘dirc’ arguments. format (str) – Format of output image. Default is ‘png’. dirc (str) – Directory for output images. Defaults to current working directory. File containing plot of Bloch sphere.
show()[source]

Display the Bloch sphere and corresponding data sets.

## non-Markovian Solvers¶

class HEOMSolver[source]

This is superclass for all solvers that use the HEOM method for calculating the dynamics evolution. There are many references for this. A good introduction, and perhaps closest to the notation used here is: DOI:10.1103/PhysRevLett.104.250401 A more canonical reference, with full derivation is: DOI: 10.1103/PhysRevA.41.6676 The method can compute open system dynamics without using any Markovian or rotating wave approximation (RWA) for systems where the bath correlations can be approximated to a sum of complex eponentials. The method builds a matrix of linked differential equations, which are then solved used the same ODE solvers as other qutip solvers (e.g. mesolve)

This class should be treated as abstract. Currently the only subclass implemented is that for the Drude-Lorentz spectral density. This covers the majority of the work that has been done using this model, and there are some performance advantages to assuming this model where it is appropriate.

There are opportunities to develop a more general spectral density code.

H_sys

Qobj – System Hamiltonian

coup_op

Qobj – Operator describing the coupling between system and bath.

coup_strength

float – Coupling strength.

temperature

float – Bath temperature, in units corresponding to planck

N_cut

int – Cutoff parameter for the bath

N_exp

int – Number of exponential terms used to approximate the bath correlation functions

planck

float – reduced Planck constant

boltzmann

float – Boltzmann’s constant

options

qutip.solver.Options – Generic solver options. If set to None the default options will be used

progress_bar

BaseProgressBar – Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation.

stats

qutip.solver.Stats – optional container for holding performance statitics If None is set, then statistics are not collected There may be an overhead in collecting statistics

exp_coeff

list of complex – Coefficients for the exponential series terms

exp_freq

list of complex – Frequencies for the exponential series terms

configure(H_sys, coup_op, coup_strength, temperature, N_cut, N_exp, planck=None, boltzmann=None, renorm=None, bnd_cut_approx=None, options=None, progress_bar=None, stats=None)[source]

Configure the solver using the passed parameters The parameters are described in the class attributes, unless there is some specific behaviour

Parameters: options (qutip.solver.Options) – Generic solver options. If set to None the default options will be used progress_bar (BaseProgressBar) – Optional instance of BaseProgressBar, or a subclass thereof, for showing the progress of the simulation. If set to None, then the default progress bar will be used Set to False for no progress bar stats (qutip.solver.Stats) – Optional instance of solver.Stats, or a subclass thereof, for storing performance statistics for the solver If set to True, then the default Stats for this class will be used Set to False for no stats
create_new_stats()[source]

Creates a new stats object suitable for use with this solver Note: this solver expects the stats object to have sections

config integrate
reset()[source]

Reset any attributes to default values

class HSolverDL(H_sys, coup_op, coup_strength, temperature, N_cut, N_exp, cut_freq, planck=1.0, boltzmann=1.0, renorm=True, bnd_cut_approx=True, options=None, progress_bar=None, stats=None)[source]

HEOM solver based on the Drude-Lorentz model for spectral density. Drude-Lorentz bath the correlation functions can be exactly analytically expressed as an infinite sum of exponentials which depend on the temperature, these are called the Matsubara terms or Matsubara frequencies

For practical computation purposes an approximation must be used based on a small number of Matsubara terms (typically < 4).

cut_freq

float – Bath spectral density cutoff frequency.

renorm

bool – Apply renormalisation to coupling terms Can be useful if using SI units for planck and boltzmann

bnd_cut_approx

bool – Use boundary cut off approximation Can be

configure(H_sys, coup_op, coup_strength, temperature, N_cut, N_exp, cut_freq, planck=None, boltzmann=None, renorm=None, bnd_cut_approx=None, options=None, progress_bar=None, stats=None)[source]

Calls configure from HEOMSolver and sets any attributes that are specific to this subclass

reset()[source]

Reset any attributes to default values

run(rho0, tlist)[source]

Function to solve for an open quantum system using the HEOM model.

Parameters: rho0 (Qobj) – Initial state (density matrix) of the system. tlist (list) – Time over which system evolves. results – Object storing all results from the simulation. qutip.solver.Result
class MemoryCascade(H_S, L1, L2, S_matrix=None, c_ops_markov=None, integrator='propagator', parallel=False, options=None)[source]

Class for running memory cascade simulations of open quantum systems with time-delayed coherent feedback.

H_S

qutip.Qobj – System Hamiltonian (can also be a Liouvillian)

L1

qutip.Qobj / list of qutip.Qobj – System operators coupling into the feedback loop. Can be a single operator or a list of operators.

L2

qutip.Qobj / list of qutip.Qobj – System operators coupling out of the feedback loop. Can be a single operator or a list of operators. L2 must have the same length as L1.

S_matrix

array – S matrix describing which operators in L1 are coupled to which operators in L2 by the feedback channel. Defaults to an n by n identity matrix where n is the number of elements in L1/L2.

c_ops_markov

qutip.Qobj / list of qutip.Qobj – Decay operators describing conventional Markovian decay channels. Can be a single operator or a list of operators.

integrator

str {‘propagator’, ‘mesolve’} – Integrator method to use. Defaults to ‘propagator’ which tends to be faster for long times (i.e., large Hilbert space).

parallel

bool – Run integrator in parallel if True. Only implemented for ‘propagator’ as the integrator method.

options

qutip.solver.Options – Generic solver options.

outfieldcorr(rho0, blist, tlist, tau, c1=None, c2=None)[source]

Compute output field expectation value <O_n(tn)...O_2(t2)O_1(t1)> for times t1,t2,... and O_i = I, b_out, b_out^dagger, b_loop, b_loop^dagger

Parameters: rho0 (qutip.Qobj) – initial density matrix or state vector (ket). blist (array_like) – List of integers specifying the field operators: 0: I (nothing) 1: b_out 2: b_out^dagger 3: b_loop 4: b_loop^dagger tlist (array_like) – list of corresponding times t1,..,tn at which to evaluate the field operators tau (float) – time-delay c1 (qutip.Qobj) – system collapse operator that couples to the in-loop field in question (only needs to be specified if self.L1 has more than one element) c2 (qutip.Qobj) – system collapse operator that couples to the output field in question (only needs to be specified if self.L2 has more than one element) expectation value of field correlation function complex
outfieldpropagator(blist, tlist, tau, c1=None, c2=None, notrace=False)[source]

Compute propagator for computing output field expectation values <O_n(tn)...O_2(t2)O_1(t1)> for times t1,t2,... and O_i = I, b_out, b_out^dagger, b_loop, b_loop^dagger

Parameters: blist (array_like) – List of integers specifying the field operators: 0: I (nothing) 1: b_out 2: b_out^dagger 3: b_loop 4: b_loop^dagger tlist (array_like) – list of corresponding times t1,..,tn at which to evaluate the field operators tau (float) – time-delay c1 (qutip.Qobj) – system collapse operator that couples to the in-loop field in question (only needs to be specified if self.L1 has more than one element) c2 (qutip.Qobj) – system collapse operator that couples to the output field in question (only needs to be specified if self.L2 has more than one element) notrace (bool {False}) – If this optional is set to True, a propagator is returned for a cascade of k systems, where $$(k-1) tau < t < k tau$$. If set to False (default), a generalized partial trace is performed and a propagator for a single system is returned. time-propagator for computing field correlation function qutip.Qobj
propagator(t, tau, notrace=False)[source]

Compute propagator for time t and time-delay tau

Parameters: t (float) – current time tau (float) – time-delay notrace (bool {False}) – If this optional is set to True, a propagator is returned for a cascade of k systems, where $$(k-1) tau < t < k tau$$. If set to False (default), a generalized partial trace is performed and a propagator for a single system is returned. time-propagator for reduced system dynamics qutip.Qobj
rhot(rho0, t, tau)[source]

Compute the reduced system density matrix $$\rho(t)$$

Parameters: rho0 (qutip.Qobj) – initial density matrix or state vector (ket) t (float) – current time tau (float) – time-delay density matrix at time $$t$$ qutip.Qobj
class TTMSolverOptions(dynmaps=None, times=[], learningtimes=[], thres=0.0, options=None)[source]

Class of options for the Transfer Tensor Method solver.

dynmaps

list of qutip.Qobj – List of precomputed dynamical maps (superoperators), or a callback function that returns the superoperator at a given time.

times

array_like – List of times $$t_n$$ at which to calculate $$\rho(t_n)$$

learningtimes

array_like – List of times $$t_k$$ to use as learning times if argument dynmaps is a callback function.

thres

float – Threshold for halting. Halts if $$||T_{n}-T_{n-1}||$$ is below treshold.

options

qutip.solver.Options – Generic solver options.

## Solver Options and Results¶

class Options(atol=1e-08, rtol=1e-06, method='adams', order=12, nsteps=1000, first_step=0, max_step=0, min_step=0, average_expect=True, average_states=False, tidy=True, num_cpus=0, norm_tol=0.001, norm_steps=5, rhs_reuse=False, rhs_filename=None, ntraj=500, gui=False, rhs_with_state=False, store_final_state=False, store_states=False, seeds=None, steady_state_average=False, normalize_output=True)[source]

Class of options for evolution solvers such as qutip.mesolve and qutip.mcsolve. Options can be specified either as arguments to the constructor:

opts = Options(order=10, ...)


or by changing the class attributes after creation:

opts = Options()
opts.order = 10


Returns options class to be used as options in evolution solvers.

atol

float {1e-8} – Absolute tolerance.

rtol

float {1e-6} – Relative tolerance.

method

str {‘adams’,’bdf’} – Integration method.

order

int {12} – Order of integrator (<=12 ‘adams’, <=5 ‘bdf’)

nsteps

int {2500} – Max. number of internal steps/call.

first_step

float {0} – Size of initial step (0 = automatic).

min_step

float {0} – Minimum step size (0 = automatic).

max_step

float {0} – Maximum step size (0 = automatic)

tidy

bool {True,False} – Tidyup Hamiltonian and initial state by removing small terms.

num_cpus

int – Number of cpus used by mcsolver (default = # of cpus).

norm_tol

float – Tolerance used when finding wavefunction norm in mcsolve.

norm_steps

int – Max. number of steps used to find wavefunction norm to within norm_tol in mcsolve.

average_states

bool {False} – Average states values over trajectories in stochastic solvers.

average_expect

bool {True} – Average expectation values over trajectories for stochastic solvers.

mc_corr_eps

float {1e-10} – Arbitrarily small value for eliminating any divide-by-zero errors in correlation calculations when using mcsolve.

ntraj

int {500} – Number of trajectories in stochastic solvers.

rhs_reuse

bool {False,True} – Reuse Hamiltonian data.

rhs_with_state

bool {False,True} – Whether or not to include the state in the Hamiltonian function callback signature.

rhs_filename

str – Name for compiled Cython file.

seeds

ndarray – Array containing random number seeds for mcsolver.

store_final_state

bool {False, True} – Whether or not to store the final state of the evolution in the result class.

store_states

bool {False, True} – Whether or not to store the state vectors or density matrices in the result class, even if expectation values operators are given. If no expectation are provided, then states are stored by default and this option has no effect.

class Result[source]

Class for storing simulation results from any of the dynamics solvers.

solver

str – Which solver was used [e.g., ‘mesolve’, ‘mcsolve’, ‘brmesolve’, ...]

times

list/array – Times at which simulation data was collected.

expect

list/array – Expectation values (if requested) for simulation.

states

array – State of the simulation (density matrix or ket) evaluated at times.

num_expect

int – Number of expectation value operators in simulation.

num_collapse

int – Number of collapse operators in simualation.

ntraj

int/list – Number of trajectories (for stochastic solvers). A list indicates that averaging of expectation values was done over a subset of total number of trajectories.

col_times

list – Times at which state collpase occurred. Only for Monte Carlo solver.

col_which

list – Which collapse operator was responsible for each collapse in col_times. Only for Monte Carlo solver.

class Stats(section_names=None)[source]

Statistical information on the solver performance Statistics can be grouped into sections. If no section names are given in the the contructor, then all statistics will be added to one section ‘main’

Parameters: section_names (list) – list of keys that will be used as keys for the sections These keys will also be used as names for the sections The text in the output can be overidden by setting the header property of the section If no names are given then one section called ‘main’ is created
sections

OrderedDict of _StatsSection – These are the sections that are created automatically on instantiation or added using add_section

header

string – Some text that will be used as the heading in the report By default there is None

total_time

float – Time in seconds for the solver to complete processing Can be None, meaning that total timing percentages will be reported

add_section()

add_count()

Add some stat that is an integer count

add_timing()

Add some timing statistics

add_message()

Add some text type for output in the report

report:

Output the statistics report to console or file.

add_count(key, value, section=None)[source]

Add value to count. If key does not already exist in section then it is created with this value. If key already exists it is increased by the give value value is expected to be an integer

Parameters: key (string) – key for the section.counts dictionary reusing a key will result in numerical addition of value value (int) – Initial value of the count, or added to an existing count section (string or class : _StatsSection) – Section which to add the count to. If None given, the default (first) section will be used
add_message(key, value, section=None, sep=';')[source]

Add value to message. If key does not already exist in section then it is created with this value. If key already exists the value is added to the message The value will be converted to a string

Parameters: key (string) – key for the section.messages dictionary reusing a key will result in concatenation of value value (int) – Initial value of the message, or added to an existing message sep (string) – Message will be prefixed with this string when concatenating section (string or class : _StatsSection) – Section which to add the message to. If None given, the default (first) section will be used
add_section(name)[source]

Add another section with the given name

Parameters: name (string) – will be used as key for sections dict will also be the header for the section section – The new section class : _StatsSection
add_timing(key, value, section=None)[source]

Add value to timing. If key does not already exist in section then it is created with this value. If key already exists it is increased by the give value value is expected to be a float, and given in seconds.

Parameters: key (string) – key for the section.timings dictionary reusing a key will result in numerical addition of value value (int) – Initial value of the timing, or added to an existing timing section (string or class : _StatsSection) – Section which to add the timing to. If None given, the default (first) section will be used
clear()[source]

Clear counts, timings and messages from all sections

report(output=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>)[source]

Report the counts, timings and messages from the sections. Sections are reported in the order that the names were supplied in the constructor. The counts, timings and messages are reported in the order that they are added to the sections The output can be written to anything that supports a write method, e.g. a file or the console (default) The output is intended to in markdown format

Parameters: output (stream) – file or console stream - anything that support write - where the output will be written
set_total_time(value, section=None)[source]

Sets the total time for the complete solve or for a specific section value is expected to be a float, and given in seconds

Parameters: value (float) – Time in seconds to complete the solver section section (string or class : _StatsSection) – Section which to set the total_time for If None given, the total_time for complete solve is set
class StochasticSolverOptions(H=None, state0=None, times=None, c_ops=[], sc_ops=[], e_ops=[], m_ops=None, args=None, ntraj=1, nsubsteps=1, d1=None, d2=None, d2_len=1, dW_factors=None, rhs=None, generate_A_ops=None, generate_noise=None, homogeneous=True, solver=None, method=None, distribution='normal', store_measurement=False, noise=None, normalize=True, options=None, progress_bar=None, map_func=None, map_kwargs=None)[source]

Class of options for stochastic solvers such as qutip.stochastic.ssesolve, qutip.stochastic.smesolve, etc. Options can be specified either as arguments to the constructor:

sso = StochasticSolverOptions(nsubsteps=100, ...)


or by changing the class attributes after creation:

sso = StochasticSolverOptions()
sso.nsubsteps = 1000


The stochastic solvers qutip.stochastic.ssesolve, qutip.stochastic.smesolve, qutip.stochastic.ssepdpsolve and qutip.stochastic.smepdpsolve all take the same keyword arguments as the constructor of these class, and internally they use these arguments to construct an instance of this class, so it is rarely needed to explicitly create an instance of this class.

H

qutip.Qobj – System Hamiltonian.

state0

qutip.Qobj – Initial state vector (ket) or density matrix.

times

list* / *array – List of times for $$t$$. Must be uniformly spaced.

c_ops

list of qutip.Qobj – List of deterministic collapse operators.

sc_ops

list of qutip.Qobj – List of stochastic collapse operators. Each stochastic collapse operator will give a deterministic and stochastic contribution to the equation of motion according to how the d1 and d2 functions are defined.

e_ops

list of qutip.Qobj – Single operator or list of operators for which to evaluate expectation values.

m_ops

list of qutip.Qobj – List of operators representing the measurement operators. The expected format is a nested list with one measurement operator for each stochastic increament, for each stochastic collapse operator.

args

dict / list – List of dictionary of additional problem-specific parameters. Implicit methods can adjust tolerance via args = {‘tol’:value}

ntraj

int – Number of trajectors.

nsubsteps

int – Number of sub steps between each time-spep given in times.

d1

function – Function for calculating the operator-valued coefficient to the deterministic increment dt.

d2

function – Function for calculating the operator-valued coefficient to the stochastic increment(s) dW_n, where n is in [0, d2_len[.

d2_len

int (default 1) – The number of stochastic increments in the process.

dW_factors

array – Array of length d2_len, containing scaling factors for each measurement operator in m_ops.

rhs

function – Function for calculating the deterministic and stochastic contributions to the right-hand side of the stochastic differential equation. This only needs to be specified when implementing a custom SDE solver.

generate_A_ops

function – Function that generates a list of pre-computed operators or super- operators. These precomputed operators are used in some d1 and d2 functions.

generate_noise

function – Function for generate an array of pre-computed noise signal.

homogeneous

bool (True) – Wheter or not the stochastic process is homogenous. Inhomogenous processes are only supported for poisson distributions.

solver

string – Name of the solver method to use for solving the stochastic equations. Valid values are: 1/2 order algorithms: ‘euler-maruyama’, ‘fast-euler-maruyama’, ‘pc-euler’ is a predictor-corrector method which is more stable than explicit methods, 1 order algorithms: ‘milstein’, ‘fast-milstein’, ‘platen’, ‘milstein-imp’ is semi-implicit Milstein method, 3/2 order algorithms: ‘taylor15’, ‘taylor15-imp’ is semi-implicit Taylor 1.5 method. Implicit methods can adjust tolerance via args = {‘tol’:value}, default is {‘tol’:1e-6}

method

string (‘homodyne’, ‘heterodyne’, ‘photocurrent’) – The name of the type of measurement process that give rise to the stochastic equation to solve. Specifying a method with this keyword argument is a short-hand notation for using pre-defined d1 and d2 functions for the corresponding stochastic processes.

distribution

string (‘normal’, ‘poission’) – The name of the distribution used for the stochastic increments.

store_measurements

bool (default False) – Whether or not to store the measurement results in the qutip.solver.SolverResult instance returned by the solver.

noise

array – Vector specifying the noise.

normalize

bool (default True) – Whether or not to normalize the wave function during the evolution.

options

qutip.solver.Options – Generic solver options.

map_func

function – A map function or managing the calls to single-trajactory solvers.

map_kwargs

dictionary – Optional keyword arguments to the map_func function function.

progress_bar

qutip.ui.BaseProgressBar – Optional progress bar class instance.

## Distribution functions¶

class Distribution(data=None, xvecs=[], xlabels=[])[source]

A class for representation spatial distribution functions.

The Distribution class can be used to prepresent spatial distribution functions of arbitray dimension (although only 1D and 2D distributions are used so far).

It is indented as a base class for specific distribution function, and provide implementation of basic functions that are shared among all Distribution functions, such as visualization, calculating marginal distributions, etc.

Parameters: data (array_like) – Data for the distribution. The dimensions must match the lengths of the coordinate arrays in xvecs. xvecs (list) – List of arrays that spans the space for each coordinate. xlabels (list) – List of labels for each coordinate.
marginal(dim=0)[source]

Calculate the marginal distribution function along the dimension dim. Return a new Distribution instance describing this reduced- dimensionality distribution.

Parameters: dim (int) – The dimension (coordinate index) along which to obtain the marginal distribution. d – A new instances of Distribution that describes the marginal distribution. Distributions
project(dim=0)[source]

Calculate the projection (max value) distribution function along the dimension dim. Return a new Distribution instance describing this reduced-dimensionality distribution.

Parameters: dim (int) – The dimension (coordinate index) along which to obtain the projected distribution. d – A new instances of Distribution that describes the projection. Distributions
visualize(fig=None, ax=None, figsize=(8, 6), colorbar=True, cmap=None, style='colormap', show_xlabel=True, show_ylabel=True)[source]

Visualize the data of the distribution in 1D or 2D, depending on the dimensionality of the underlaying distribution.

Parameters:

fig
: matplotlib Figure instance
If given, use this figure instance for the visualization,
ax
: matplotlib Axes instance
If given, render the visualization using this axis instance.
figsize
: tuple
Size of the new Figure instance, if one needs to be created.
colorbar: Bool
Whether or not the colorbar (in 2D visualization) should be used.
cmap: matplotlib colormap instance
If given, use this colormap for 2D visualizations.
style
: string
Type of visualization: ‘colormap’ (default) or ‘surface’.
Returns: fig, ax – A tuple of matplotlib figure and axes instances. tuple
class WignerDistribution(rho=None, extent=[[-5, 5], [-5, 5]], steps=250)[source]
class QDistribution(rho=None, extent=[[-5, 5], [-5, 5]], steps=250)[source]
class TwoModeQuadratureCorrelation(state=None, theta1=0.0, theta2=0.0, extent=[[-5, 5], [-5, 5]], steps=250)[source]
update(state)[source]

calculate probability distribution for quadrature measurement outcomes given a two-mode wavefunction or density matrix

update_psi(psi)[source]

calculate probability distribution for quadrature measurement outcomes given a two-mode wavefunction

update_rho(rho)[source]

calculate probability distribution for quadrature measurement outcomes given a two-mode density matrix

class HarmonicOscillatorWaveFunction(psi=None, omega=1.0, extent=[-5, 5], steps=250)[source]
update(psi)[source]

Calculate the wavefunction for the given state of an harmonic oscillator

class HarmonicOscillatorProbabilityFunction(rho=None, omega=1.0, extent=[-5, 5], steps=250)[source]
update(rho)[source]

Calculate the probability function for the given state of an harmonic oscillator (as density matrix)

## Quantum information processing¶

class Gate(name, targets=None, controls=None, arg_value=None, arg_label=None)[source]

Representation of a quantum gate, with its required parametrs, and target and control qubits.

class QubitCircuit(N, reverse_states=True)[source]

Representation of a quantum program/algorithm, maintaining a sequence of gates.

add_1q_gate(name, start=0, end=None, qubits=None, arg_value=None, arg_label=None)[source]

Adds a single qubit gate with specified parameters on a variable number of qubits in the circuit. By default, it applies the given gate to all the qubits in the register.

Parameters: name (String) – Gate name. start (Integer) – Starting location of qubits. end (Integer) – Last qubit for the gate. qubits (List) – Specific qubits for applying gates. arg_value (Float) – Argument value(phi). arg_label (String) – Label for gate representation.
add_circuit(qc, start=0)[source]

Adds a block of a qubit circuit to the main circuit. Globalphase gates are not added.

Parameters: qc (QubitCircuit) – The circuit block to be added to the main circuit. start (Integer) – The qubit on which the first gate is applied.
add_gate(gate, targets=None, controls=None, arg_value=None, arg_label=None)[source]

Adds a gate with specified parameters to the circuit.

Parameters: gate (String or Gate) – Gate name. If gate is an instance of Gate, parameters are unpacked and added. targets (List) – Gate targets. controls (List) – Gate controls. arg_value (Float) – Argument value(phi). arg_label (String) – Label for gate representation.
adjacent_gates()[source]

Method to resolve two qubit gates with non-adjacent control/s or target/s in terms of gates with adjacent interactions.

Returns: qc – Returns QubitCircuit of the gates for the qubit circuit with the resolved non-adjacent gates. QubitCircuit
propagators()[source]

Propagator matrix calculator for N qubits returning the individual steps as unitary matrices operating from left to right.

Returns: U_list – Returns list of unitary matrices for the qubit circuit. list
remove_gate(index=None, end=None, name=None, remove='first')[source]

Removes a gate from a specific index or between two indexes or the first, last or all instances of a particular gate.

Parameters: index (Integer) – Location of gate to be removed. name (String) – Gate name to be removed. remove (String) – If first or all gate are to be removed.
resolve_gates(basis=['CNOT', 'RX', 'RY', 'RZ'])[source]

Unitary matrix calculator for N qubits returning the individual steps as unitary matrices operating from left to right in the specified basis.

Parameters: basis (list.) – Basis of the resolved circuit. qc – Returns QubitCircuit of resolved gates for the qubit circuit in the desired basis. QubitCircuit
reverse_circuit()[source]

Reverses an entire circuit of unitary gates.

Returns: qc – Returns QubitCircuit of resolved gates for the qubit circuit in the reverse order. QubitCircuit
class CircuitProcessor(N, correct_global_phase)[source]

Base class for representation of the physical implementation of a quantum program/algorithm on a specified qubit system.

adjacent_gates(qc, setup)[source]

Function to take a quantum circuit/algorithm and convert it into the optimal form/basis for the desired physical system.

Parameters: qc (QubitCircuit) – Takes the quantum circuit to be implemented. setup (String) – Takes the nature of the spin chain; linear or circular. qc – The resolved circuit representation. QubitCircuit
get_ops_and_u()[source]

Returns the Hamiltonian operators and corresponding values by stacking them together.

get_ops_labels()[source]

Returns the Hamiltonian operators and corresponding labels by stacking them together.

load_circuit(qc)[source]

Translates an abstract quantum circuit to its corresponding Hamiltonian for a specific model.

Parameters: qc (QubitCircuit) – Takes the quantum circuit to be implemented.
optimize_circuit(qc)[source]

Function to take a quantum circuit/algorithm and convert it into the optimal form/basis for the desired physical system.

Parameters: qc (QubitCircuit) – Takes the quantum circuit to be implemented. qc – The optimal circuit representation. QubitCircuit
plot_pulses()[source]

Maps the physical interaction between the circuit components for the desired physical system.

Returns: fig, ax – Maps the physical interaction between the circuit components. Figure
pulse_matrix()[source]

Generates the pulse matrix for the desired physical system.

Returns: Returns the total time and label for every operation. t, u, labels
run(qc=None)[source]

Generates the propagator matrix by running the Hamiltonian for the appropriate time duration for the desired physical system.

Parameters: qc (QubitCircuit) – Takes the quantum circuit to be implemented. U_list – The propagator matrix obtained from the physical implementation. list
run_state(qc=None, states=None)[source]

Generates the propagator matrix by running the Hamiltonian for the appropriate time duration for the desired physical system with the given initial state of the qubit register.

Parameters: qc (QubitCircuit) – Takes the quantum circuit to be implemented. states (Qobj) – Initial state of the qubits in the register. U_list – The propagator matrix obtained from the physical implementation. list
class SpinChain(N, correct_global_phase=True, sx=None, sz=None, sxsy=None)[source]

Representation of the physical implementation of a quantum program/algorithm on a spin chain qubit system.

adjacent_gates(qc, setup='linear')[source]

Method to resolve 2 qubit gates with non-adjacent control/s or target/s in terms of gates with adjacent interactions for linear/circular spin chain system.

Parameters: qc (QubitCircuit) – The circular spin chain circuit to be resolved setup (Boolean) – Linear of Circular spin chain setup qc – Returns QubitCircuit of resolved gates for the qubit circuit in the desired basis. QubitCircuit
class LinearSpinChain(N, correct_global_phase=True, sx=None, sz=None, sxsy=None)[source]

Representation of the physical implementation of a quantum program/algorithm on a spin chain qubit system arranged in a linear formation. It is a sub-class of SpinChain.

class CircularSpinChain(N, correct_global_phase=True, sx=None, sz=None, sxsy=None)[source]

Representation of the physical implementation of a quantum program/algorithm on a spin chain qubit system arranged in a circular formation. It is a sub-class of SpinChain.

class DispersivecQED(N, correct_global_phase=True, Nres=None, deltamax=None, epsmax=None, w0=None, wq=None, eps=None, delta=None, g=None)[source]

Representation of the physical implementation of a quantum program/algorithm on a dispersive cavity-QED system.

dispersive_gate_correction(qc1, rwa=True)[source]

Method to resolve ISWAP and SQRTISWAP gates in a cQED system by adding single qubit gates to get the correct output matrix.

Parameters: qc (Qobj) – The circular spin chain circuit to be resolved rwa (Boolean) – Specify if RWA is used or not. qc – Returns QubitCircuit of resolved gates for the qubit circuit in the desired basis. QubitCircuit

## Optimal control¶

class Optimizer(config, dyn, params=None)[source]

Base class for all control pulse optimisers. This class should not be instantiated, use its subclasses This class implements the fidelity, gradient and interation callback functions. All subclass objects must be initialised with a

OptimConfig instance - various configuration options Dynamics instance - describes the dynamics of the (quantum) system

to be control optimised
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.

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 pulse optim algorithm that is GRAPE or CRAB

disp_conv_msg

bool – Set true to display a convergence message (for scipy.optimize.minimize methods anyway)

optim_method

string – a scipy.optimize.minimize method that will be used to optimise the pulse for minimum fidelity error

method_params

Dictionary – Options for the optim_method. Note that where there is an equivalent attribute of this instance or the termination_conditions (for example maxiter) it will override an value in these options

approx_grad

bool – If set True then the method will approximate the gradient itself (if it has requirement and facility for this) This will mean that the fid_err_grad_wrapper will not get called Note it should be left False when using the Dynamics to calculate approximate gradients Note it is set True automatically when the alg is CRAB

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

bounds

List of floats – Bounds for the parameters. If not set before the run_optimization call then the list is built automatically based on the amp_lbound and amp_ubound attributes. Setting this attribute directly allows specific bounds to be set for individual parameters. Note: Only some methods use bounds

dynamics

Dynamics (subclass instance) – describes the dynamics of the (quantum) system to be control optimised (see Dynamics classes for details)

config

OptimConfig instance – various configuration options (see OptimConfig for details)

termination_conditions

TerminationCondition instance – attributes determine when the optimisation will end

pulse_generator

PulseGen (subclass instance) – (can be) used to create initial pulses not used by the class, but set by pulseoptim.create_pulse_optimizer

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 Dynamics instance

dump

dump.OptimDump – Container for data dumped during the optimisation. Can be set by specifying the dumping level or set directly. Note this is mainly intended for user and a development debugging but could be used for status information during a long optimisation.

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 optimisation dumping will be set to SUMMARY during init_optim 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 optim_params. If dump is None then will return None or will set dumping to SUMMARY when setting a path

iter_summary

OptimIterSummary – Summary of the most recent iteration. Note this is only set if dummping is on

apply_method_params(params=None)[source]

Loops through all the method_params (either passed here or the method_params attribute) If the name matches an attribute of this object or the termination conditions object, then the value of this attribute is set. Otherwise it is assumed to a method_option for the scipy.optimize.minimize function

apply_params(params=None)[source]

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.

dumping
The level of data dumping that will occur during the optimisation
• NONE : No processing data dumped (Default)
• SUMMARY : A summary at each iteration will be recorded
• FULL : All logs will be generated and 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 logs are dumped

fid_err_func_wrapper(*args)[source]

Get the fidelity error achieved using the ctrl amplitudes passed in as the first argument.

This is called by generic optimisation algorithm as the func to the minimised. The argument is the current variable values, i.e. control amplitudes, passed as a flat array. Hence these are reshaped as [nTimeslots, n_ctrls] and then used to update the stored ctrl values (if they have changed)

The error is checked against the target, and the optimisation is terminated if the target has been achieved.

fid_err_grad_wrapper(*args)[source]

Get the gradient of the fidelity error with respect to all of the variables, i.e. the ctrl amplidutes in each timeslot

This is called by generic optimisation algorithm as the gradients of func to the minimised wrt the variables. The argument is the current variable values, i.e. control amplitudes, passed as a flat array. Hence these are reshaped as [nTimeslots, n_ctrls] and then used to update the stored ctrl values (if they have changed)

Although the optimisation algorithms have a check within them for function convergence, i.e. local minima, the sum of the squares of the normalised gradient is checked explicitly, and the optimisation is terminated if this is below the min_gradient_norm condition

init_optim(term_conds)[source]

Check optimiser attribute status and passed parameters before running the optimisation. This is called by run_optimization, but could called independently to check the configuration.

iter_step_callback_func(*args)[source]

Check the elapsed wall time for the optimisation run so far. Terminate if this has exceeded the maximum allowed time

run_optimization(term_conds=None)[source]

This default function optimisation method is a wrapper to the scipy.optimize.minimize function.

It will attempt to minimise the fidelity error with respect to some parameters, which are determined by _get_optim_var_vals (see below)

The optimisation end when one of the passed termination conditions has been met, e.g. target achieved, wall time, or function call or iteration count exceeded. Note these conditions include gradient minimum met (local minima) for methods that use a gradient.

The function minimisation method is taken from the optim_method attribute. Note that not all of these methods have been tested. Note that some of these use a gradient and some do not. See the scipy documentation for details. Options specific to the method can be passed setting the method_params attribute.

If the parameter term_conds=None, then the termination_conditions attribute must already be set. It will be overwritten if the parameter is not None

The result is returned in an OptimResult object, which includes the final fidelity, time evolution, reason for termination etc

class OptimizerBFGS(config, dyn, params=None)[source]

Implements the run_optimization method using the BFGS algorithm

run_optimization(term_conds=None)[source]

Optimise the control pulse amplitudes to minimise the fidelity error using the BFGS (Broyden–Fletcher–Goldfarb–Shanno) algorithm The optimisation end when one of the passed termination conditions has been met, e.g. target achieved, gradient minimum met (local minima), wall time / iteration count exceeded.

Essentially this is wrapper to the: scipy.optimize.fmin_bfgs function

If the parameter term_conds=None, then the termination_conditions attribute must already be set. It will be overwritten if the parameter is not None

The result is returned in an OptimResult object, which includes the final fidelity, time evolution, reason for termination etc

class OptimizerLBFGSB(config, dyn, params=None)[source]

Implements the run_optimization method using the L-BFGS-B algorithm

max_metric_corr

integer – The maximum number of variable metric corrections used to define the limited memory matrix. That is the number of previous gradient values that are used to approximate the Hessian see the scipy.optimize.fmin_l_bfgs_b documentation for description of m argument

init_optim(term_conds)[source]

Check optimiser attribute status and passed parameters before running the optimisation. This is called by run_optimization, but could called independently to check the configuration.

run_optimization(term_conds=None)[source]

Optimise the control pulse amplitudes to minimise the fidelity error using the L-BFGS-B algorithm, which is the constrained (bounded amplitude values), limited memory, version of the Broyden–Fletcher–Goldfarb–Shanno algorithm.

The optimisation end when one of the passed termination conditions has been met, e.g. target achieved, gradient minimum met (local minima), wall time / iteration count exceeded.

Essentially this is wrapper to the: scipy.optimize.fmin_l_bfgs_b function This in turn is a warpper for well established implementation of the L-BFGS-B algorithm written in Fortran, which is therefore very fast. See SciPy documentation for credit and details on this function.

If the parameter term_conds=None, then the termination_conditions attribute must already be set. It will be overwritten if the parameter is not None

The result is returned in an OptimResult object, which includes the final fidelity, time evolution, reason for termination etc

class OptimizerCrab(config, dyn, params=None)[source]

Optimises the pulse using the CRAB algorithm [1]. It uses the scipy.optimize.minimize function with the method specified by the optim_method attribute. See Optimizer.run_optimization for details It minimises the fidelity error function with respect to the CRAB basis function coefficients.

AJGP ToDo: Add citation here

init_optim(term_conds)[source]

Check optimiser attribute status and passed parameters before running the optimisation. This is called by run_optimization, but could called independently to check the configuration.

class OptimizerCrabFmin(config, dyn, params=None)[source]

Optimises the pulse using the CRAB algorithm [1, 2]. It uses the scipy.optimize.fmin function which is effectively a wrapper for the Nelder-mead method. It minimises the fidelity error function with respect to the CRAB basis function coefficients. This is the default Optimizer for CRAB.

Notes

[1] P. Doria, T. Calarco & S. Montangero. Phys. Rev. Lett. 106,
190501 (2011).

[2] T. Caneva, T. Calarco, & S. Montangero. Phys. Rev. A 84, 022326 (2011).

run_optimization(term_conds=None)[source]

This function optimisation method is a wrapper to the scipy.optimize.fmin function.

It will attempt to minimise the fidelity error with respect to some parameters, which are determined by _get_optim_var_vals which in the case of CRAB are the basis function coefficients

The optimisation end when one of the passed termination conditions has been met, e.g. target achieved, wall time, or function call or iteration count exceeded. Specifically to the fmin method, the optimisation will stop when change parameter values is less than xtol or the change in function value is below ftol.

If the parameter term_conds=None, then the termination_conditions attribute must already be set. It will be overwritten if the parameter is not None

The result is returned in an OptimResult object, which includes the final fidelity, time evolution, reason for termination etc

class OptimIterSummary[source]

A summary of the most recent iteration of the pulse optimisation

iter_num

int – Iteration number of the pulse optimisation

fid_func_call_num

int – Fidelity function call number of the pulse optimisation

grad_func_call_num

int – Gradient function call number of the pulse optimisation

fid_err

float – Fidelity error

grad_norm

float – fidelity gradient (wrt the control parameters) vector norm that is the magnitude of the gradient

wall_time

float – Time spent computing the pulse optimisation so far (in seconds of elapsed time)

class TerminationConditions[source]

Base class for all termination conditions Used to determine when to stop the optimisation algorithm Note different subclasses should be used to match the type of optimisation being used

fid_err_targ

float – Target fidelity error

fid_goal

float – goal fidelity, e.g. 1 - self.fid_err_targ It its typical to set this for unitary systems

max_wall_time

float – # maximum time for optimisation (seconds)

min_gradient_norm

float – Minimum normalised gradient after which optimisation will terminate

max_iterations

integer – Maximum iterations of the optimisation algorithm

max_fid_func_calls

integer – Maximum number of calls to the fidelity function during the optimisation algorithm

accuracy_factor

float – Determines the accuracy of the result. Typical values for accuracy_factor are: 1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy scipy.optimize.fmin_l_bfgs_b factr argument. Only set for specific methods (fmin_l_bfgs_b) that uses this Otherwise the same thing is passed as method_option ftol (although the scale is different) Hence it is not defined here, but may be set by the user

class OptimResult[source]

Attributes give the result of the pulse optimisation attempt

termination_reason

string – Description of the reason for terminating the optimisation

fidelity

float – final (normalised) fidelity that was achieved

initial_fid_err

float – fidelity error before optimisation starting

fid_err

float – final fidelity error that was achieved

goal_achieved

boolean – True is the fidely error achieved was below the target

grad_norm_final

float – Final value of the sum of the squares of the (normalised) fidelity error gradients

grad_norm_min_reached

float – True if the optimisation terminated due to the minimum value of the gradient being reached

num_iter

integer – Number of iterations of the optimisation algorithm completed

max_iter_exceeded

boolean – True if the iteration limit was reached

max_fid_func_exceeded

boolean – True if the fidelity function call limit was reached

wall_time

float – time elapsed during the optimisation

wall_time_limit_exceeded

boolean – True if the wall time limit was reached

time

array[num_tslots+1] of float – Time are the start of each timeslot with the final value being the total evolution time

initial_amps

array[num_tslots, n_ctrls] – The amplitudes at the start of the optimisation

final_amps

array[num_tslots, n_ctrls] – The amplitudes at the end of the optimisation

evo_full_final

Qobj – The evolution operator from t=0 to t=T based on the final amps

stats

Stats – Object contaning the stats for the run (if any collected)

optimizer

Optimizer – Instance of the Optimizer used to generate the result

class Dynamics(optimconfig, params=None)[source]

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

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, or (in theory) any other representaion that supports typical matrix methods (e.g. dot) ndarray performs best for smaller quantum systems. 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

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

apply_params(params=None)[source]

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.

combine_dyn_gen(k)[source]

Computes the dynamics generator for a given timeslot The is the combined Hamiltion for unitary systems

compute_evolution()[source]

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

dumping

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!

dyn_gen

List of combined dynamics generators (Qobj) for each timeslot

dyn_gen_phase

Some preop that is applied to the dyn_gen before expontiating to get the propagator

flag_system_changed()[source]

Flag evolution, fidelity and gradients as needing recalculation

full_evo

Full evolution - time evolution at final time slot

fwd_evo

List of evolution operators (Qobj) from the initial to the given timeslot

get_ctrl_dyn_gen(j)[source]

Get the dynamics generator for the control Not implemented in the base class. Choose a subclass

get_drift_dim()[source]

Returns the size of the matrix that defines the drift dynamics that is assuming the drift is NxN, then this returns N

get_dyn_gen(k)[source]

Get the combined dynamics generator for the timeslot Not implemented in the base class. Choose a subclass

get_num_ctrls()[source]

calculate the of controls from the length of the control list sets the num_ctrls property, which can be used alternatively subsequently

init_timeslots()[source]

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

initialize_controls(amps, init_tslots=True)[source]

Set the initial control amplitudes and time slices Note this must be called after the configuration is complete before any dynamics can be calculated

num_ctrls

calculate the of controls from the length of the control list sets the num_ctrls property, which can be used alternatively subsequently

onto_evo

List of evolution operators (Qobj) from the initial to the given timeslot

onwd_evo

List of evolution operators (Qobj) from the initial to the given timeslot

prop

List of propagators (Qobj) for each timeslot

prop_grad

Array of propagator gradients (Qobj) for each timeslot, control

refresh_drift_attribs()[source]

Reset the dyn_shape, dyn_dims and time_depend_drift attribs

save_amps(file_name=None, times=None, amps=None, verbose=False)[source]

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
unitarity_check()[source]

Checks whether all propagators are unitary

update_ctrl_amps(new_amps)[source]

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

class DynamicsGenMat(optimconfig, params=None)[source]

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

class DynamicsUnitary(optimconfig, params=None)[source]

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)

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

check_unitarity()[source]

Checks whether all propagators are unitary For propagators found not to be unitary, the potential underlying causes are investigated.

class DynamicsSymplectic(optimconfig, params=None)[source]

Symplectic systems This is the subclass to use for systems where the dynamics is described by symplectic matrices, e.g. coupled oscillators, quantum optics

omega

array[drift_dyn_gen.shape] – matrix used in the calculation of propagators (time evolution) with symplectic systems.

dyn_gen_phase

The prephasing operator for the symplectic group generators usually refered to as Omega

class PropagatorComputer(dynamics, params=None)[source]

Base for all Propagator Computer classes that are used to calculate the propagators, and also the propagator gradient when exact gradient methods are used Note: they must be instantiated with a Dynamics object, that is the container for the data that the functions operate on This base class cannot be used directly. See subclass descriptions and choose the appropriate one for the application

log_level

integer – level of messaging output from the logger. Options are attributes of qutip_utils.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

grad_exact

boolean – indicates whether the computer class instance is capable of computing propagator gradients. It is used to determine whether to create the Dynamics prop_grad array

apply_params(params=None)[source]

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.

reset()[source]

reset any configuration data

class PropCompApproxGrad(dynamics, params=None)[source]

This subclass can be used when the propagator is calculated simply by expm of the dynamics generator, i.e. when gradients will be calculated using approximate methods.

reset()[source]

reset any configuration data

class PropCompDiag(dynamics, params=None)[source]

Coumputes the propagator exponentiation using diagonalisation of of the dynamics generator

reset()[source]

reset any configuration data

class PropCompFrechet(dynamics, params=None)[source]
Frechet method for calculating the propagator:
exponentiating the combined dynamics generator

and the propagator gradient It should work for all systems, e.g. unitary, open, symplectic There are other PropagatorComputer subclasses that may be more efficient

class FidelityComputer(dynamics, params=None)[source]

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

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

dimensional_norm

float – Normalisation constant

fid_norm_func

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

grad_norm_func

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

uses_onwd_evo

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

uses_onto_evo

boolean

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

float – Last computed value of the fidelity error

fidelity

float – Last computed value of the normalised fidelity

fidelity_current

boolean – 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_norm

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

fid_err_grad_current

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

apply_params(params=None)[source]

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()[source]

clear any temporarily held status data

flag_system_changed()[source]

Flag fidelity and gradients as needing recalculation

get_fid_err()[source]

returns the absolute distance from the maximum achievable fidelity

get_fid_err_gradient()[source]

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

init_comp()[source]

initialises the computer based on the configuration of the Dynamics

reset()[source]

reset any configuration data and clear any temporarily held status data

class FidCompUnitary(dynamics, params=None)[source]

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)

phase_option

string

determines how global phase is treated in fidelity calculations:
PSU - global phase ignored SU - global phase included
fidelity_prenorm

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

fidelity_prenorm_current

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

compute_fid_grad()[source]

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()[source]

Flag fidelity and gradients as needing recalculation

get_fid_err()[source]

Gets the absolute error in the fidelity

get_fid_err_gradient()[source]

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()[source]

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()[source]

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()[source]

Check configuration and initialise the normalisation

init_normalization()[source]

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_PSU(A)[source]
normalize_SU(A)[source]
normalize_gradient_PSU(grad)[source]

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

normalize_gradient_SU(grad)[source]

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

set_phase_option(phase_option=None)[source]

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

class FidCompTraceDiff(dynamics, params=None)[source]

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

scale_factor

float – 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()[source]

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()[source]

Gets the absolute error in the fidelity

get_fid_err_gradient()[source]

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()[source]

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

class FidCompTraceDiffApprox(dynamics, params=None)[source]

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

epsilon

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

compute_fid_err_grad()[source]

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

class TimeslotComputer(dynamics, params=None)[source]

Base class for all Timeslot Computers Note: this must be instantiated with a Dynamics object, that is the container for the data that the methods operate on

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

evo_comp_summary

EvoCompSummary – A summary of the most recent evolution computation Used in the stats and dump Will be set to None if neither stats or dump are set

apply_params(params=None)[source]

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.

dump_current()[source]

Store a copy of the current time evolution

class TSlotCompUpdateAll(dynamics, params=None)[source]

Timeslot Computer - Update All Updates all dynamics generators, propagators and evolutions when ctrl amplitudes are updated

compare_amps(new_amps)[source]

Determine if any amplitudes have changed. If so, then mark the timeslots as needing recalculation Returns: True if amplitudes are the same, False if they have changed

get_timeslot_for_fidelity_calc()[source]

Returns the timeslot index that will be used calculate current fidelity value. This (default) method simply returns the last timeslot

recompute_evolution()[source]

Recalculates the evolution operators. Dynamics generators (e.g. Hamiltonian) and prop (propagators) are calculated as necessary

class PulseGen(dyn=None, params=None)[source]

Pulse generator Base class for all Pulse generators The object can optionally be instantiated with a Dynamics object, in which case the timeslots and amplitude scaling and offset are copied from that. Otherwise the class can be used independently by setting: tau (array of timeslot durations) or num_tslots and pulse_time for equally spaced timeslots

num_tslots

integer – Number of timeslots, aka timeslices (copied from Dynamics if given)

pulse_time

float – total duration of the pulse (copied from Dynamics.evo_time if given)

scaling

float – linear scaling applied to the pulse (copied from Dynamics.initial_ctrl_scaling if given)

offset

float – linear offset applied to the pulse (copied from Dynamics.initial_ctrl_offset if given)

tau

array[num_tslots] of float – Duration of each timeslot (copied from Dynamics if given)

lbound

float – Lower boundary for the pulse amplitudes Note that the scaling and offset attributes can be used to fully bound the pulse for all generators except some of the random ones This bound (if set) may result in additional shifting / scaling Default is -Inf

ubound

float – Upper boundary for the pulse amplitudes Note that the scaling and offset attributes can be used to fully bound the pulse for all generators except some of the random ones This bound (if set) may result in additional shifting / scaling Default is Inf

periodic

boolean – True if the pulse generator produces periodic pulses

random

boolean – True if the pulse generator produces random pulses

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

apply_params(params=None)[source]

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

gen_pulse()[source]

returns the pulse as an array of vales for each timeslot Must be implemented by subclass

init_pulse()[source]

Initialise the pulse parameters

reset()[source]

reset attributes to default values

class PulseGenRandom(dyn=None, params=None)[source]

Generates random pulses as simply random values for each timeslot

gen_pulse()[source]

Generate a pulse of random values between 1 and -1 Values are scaled using the scaling property and shifted using the offset property Returns the pulse as an array of vales for each timeslot

class PulseGenZero(dyn=None, params=None)[source]

Generates a flat pulse

gen_pulse()[source]

Generate a pulse with the same value in every timeslot. The value will be zero, unless the offset is not zero, in which case it will be the offset

class PulseGenLinear(dyn=None, params=None)[source]

Generates linear pulses

gradient

float – Gradient of the line. Note this is calculated from the start_val and end_val if these are given

start_val

float – Start point of the line. That is the starting amplitude

end_val

float – End point of the line. That is the amplitude at the start of the last timeslot

gen_pulse(gradient=None, start_val=None, end_val=None)[source]

Generate a linear pulse using either the gradient and start value or using the end point to calulate the gradient Note that the scaling and offset parameters are still applied, so unless these values are the default 1.0 and 0.0, then the actual gradient etc will be different Returns the pulse as an array of vales for each timeslot

init_pulse(gradient=None, start_val=None, end_val=None)[source]

Calculate the gradient if pulse is defined by start and end point values

reset()[source]

reset attributes to default values

class PulseGenPeriodic(dyn=None, params=None)[source]

Intermediate class for all periodic pulse generators All of the periodic pulses range from -1 to 1 All have a start phase that can be set between 0 and 2pi

num_waves

float – Number of complete waves (cycles) that occur in the pulse. wavelen and freq calculated from this if it is given

wavelen

float – Wavelength of the pulse (assuming the speed is 1) freq is calculated from this if it is given

freq

float – Frequency of the pulse

start_phase

float – Phase of the pulse signal when t=0

init_pulse(num_waves=None, wavelen=None, freq=None, start_phase=None)[source]

Calculate the wavelength, frequency, number of waves etc from the each other and the other parameters If num_waves is given then the other parameters are worked from this Otherwise if the wavelength is given then it is the driver Otherwise the frequency is used to calculate wavelength and num_waves

reset()[source]

reset attributes to default values

class PulseGenSine(dyn=None, params=None)[source]

Generates sine wave pulses

gen_pulse(num_waves=None, wavelen=None, freq=None, start_phase=None)[source]

Generate a sine wave pulse If no params are provided then the class object attributes are used. If they are provided, then these will reinitialise the object attribs. returns the pulse as an array of vales for each timeslot

class PulseGenSquare(dyn=None, params=None)[source]

Generates square wave pulses

gen_pulse(num_waves=None, wavelen=None, freq=None, start_phase=None)[source]

Generate a square wave pulse If no parameters are pavided then the class object attributes are used. If they are provided, then these will reinitialise the object attribs

class PulseGenSaw(dyn=None, params=None)[source]

Generates saw tooth wave pulses

gen_pulse(num_waves=None, wavelen=None, freq=None, start_phase=None)[source]

Generate a saw tooth wave pulse If no parameters are pavided then the class object attributes are used. If they are provided, then these will reinitialise the object attribs

class PulseGenTriangle(dyn=None, params=None)[source]

Generates triangular wave pulses

gen_pulse(num_waves=None, wavelen=None, freq=None, start_phase=None)[source]

Generate a sine wave pulse If no parameters are pavided then the class object attributes are used. If they are provided, then these will reinitialise the object attribs

class PulseGenGaussian(dyn=None, params=None)[source]

Generates pulses with a Gaussian profile

gen_pulse(mean=None, variance=None)[source]

Generate a pulse with Gaussian shape. The peak is centre around the mean and the variance determines the breadth The scaling and offset attributes are applied as an amplitude and fixed linear offset. Note that the maximum amplitude will be scaling + offset.

reset()[source]

reset attributes to default values

class PulseGenGaussianEdge(dyn=None, params=None)[source]

Generate pulses with inverted Gaussian ramping in and out It’s intended use for a ramping modulation, which is often required in experimental setups.

decay_time

float – Determines the ramping rate. It is approximately the time required to bring the pulse to full amplitude It is set to 1/10 of the pulse time by default

gen_pulse(decay_time=None)[source]

Generate a pulse that starts and ends at zero and 1.0 in between then apply scaling and offset The tailing in and out is an inverted Gaussian shape

reset()[source]

reset attributes to default values

class PulseGenCrab(dyn=None, num_coeffs=None, params=None)[source]

Base class for all CRAB pulse generators Note these are more involved in the optimisation process as they are used to produce piecewise control amplitudes each time new optimisation parameters are tried

num_coeffs

integer – Number of coefficients used for each basis function

num_basis_funcs

integer – Number of basis functions In this case set at 2 and should not be changed

coeffs

float array[num_coeffs, num_basis_funcs] – The basis coefficient values

randomize_coeffs

bool – If True (default) then the coefficients are set to some random values when initialised, otherwise they will all be equal to self.scaling

estimate_num_coeffs(dim)[source]

Estimate the number coefficients based on the dimensionality of the system. :returns: num_coeffs – estimated number of coefficients :rtype: int

get_optim_var_vals()[source]

Get the parameter values to be optimised :returns: :rtype: list (or 1d array) of floats

init_coeffs(num_coeffs=None)[source]

Generate the initial ceofficent values.

Parameters: num_coeffs (integer) – Number of coefficients used for each basis function If given this overides the default and sets the attribute of the same name.
init_pulse(num_coeffs=None)[source]

Set the initial freq and coefficient values

reset()[source]

reset attributes to default values

set_optim_var_vals(param_vals)[source]

Set the values of the any of the pulse generation parameters based on new values from the optimisation method Typically this will be the basis coefficients

class PulseGenCrabFourier(dyn=None, num_coeffs=None, params=None)[source]

Generates a pulse using the Fourier basis functions, i.e. sin and cos

freqs

float array[num_coeffs] – Frequencies for the basis functions

randomize_freqs

bool – If True (default) the some random offset is applied to the frequencies

gen_pulse(coeffs=None)[source]

Generate a pulse using the Fourier basis with the freqs and coeffs attributes.

Parameters: coeffs (float array[num_coeffs, num_basis_funcs]) – The basis coefficient values If given this overides the default and sets the attribute of the same name.
init_freqs()[source]

Generate the frequencies These are the Fourier harmonics with a uniformly distributed random offset

init_pulse(num_coeffs=None)[source]

Set the initial freq and coefficient values

reset()[source]

reset attributes to default values

class Stats[source]

Base class for all optimisation statistics Used for configurations where all timeslots are updated each iteration e.g. exact gradients Note that all times are generated using timeit.default_timer() and are in seconds

dyn_gen_name

string – Text used in some report functions. Makes sense to set it to ‘Hamiltonian’ when using unitary dynamics Default is simply ‘dynamics generator’

num_iter

integer – Number of iterations of the optimisation algorithm

wall_time_optim_start

float – Start time for the optimisation

wall_time_optim_end

float – End time for the optimisation

wall_time_optim

float – Time elasped during the optimisation

wall_time_dyn_gen_compute

float – Total wall (elasped) time computing combined dynamics generator (for example combining drift and control Hamiltonians)

wall_time_prop_compute

float – Total wall (elasped) time computing propagators, that is the time evolution from one timeslot to the next Includes calculating the propagator gradient for exact gradients

wall_time_fwd_prop_compute

float – Total wall (elasped) time computing combined forward propagation, that is the time evolution from the start to a specific timeslot. Excludes calculating the propagators themselves

wall_time_onwd_prop_compute

float – Total wall (elasped) time computing combined onward propagation, that is the time evolution from a specific timeslot to the end time. Excludes calculating the propagators themselves

wall_time_gradient_compute

float – Total wall (elasped) time computing the fidelity error gradient. Excludes calculating the propagator gradients (in exact gradient methods)

num_fidelity_func_calls

integer – Number of calls to fidelity function by the optimisation algorithm

num_grad_func_calls

integer – Number of calls to gradient function by the optimisation algorithm

num_tslot_recompute

integer – Number of time the timeslot evolution is recomputed (It is only computed if any amplitudes changed since the last call)

num_fidelity_computes

integer – Number of time the fidelity is computed (It is only computed if any amplitudes changed since the last call)

num_grad_computes

integer – Number of time the gradient is computed (It is only computed if any amplitudes changed since the last call)

num_ctrl_amp_updates

integer – Number of times the control amplitudes are updated

mean_num_ctrl_amp_updates_per_iter

float – Mean number of control amplitude updates per iteration

num_timeslot_changes

integer – Number of times the amplitudes of a any control in a timeslot changes

mean_num_timeslot_changes_per_update

float – Mean average number of timeslot amplitudes that are changed per update

num_ctrl_amp_changes

integer – Number of times individual control amplitudes that are changed

mean_num_ctrl_amp_changes_per_update

float – Mean average number of control amplitudes that are changed per update

calculate()[source]

Perform the calculations (e.g. averages) that are required on the stats Should be called before calling report

report()[source]

Print a report of the stats to the console

class Dump[source]

A container for dump items. The lists for dump items is depends on the type Note: abstract class

parent

some control object (Dynamics or Optimizer) – aka the host. Object that generates the data that is dumped and is host to this dump object.

dump_dir

str – directory where files (if any) will be written out the path and be relative or absolute use ~/ to specify user home directory Note: files are only written when write_to_file is True of writeout is called explicitly Defaults to ~/.qtrl_dump

level

string – level of data dumping: SUMMARY, FULL or CUSTOM See property docstring for details Set automatically if dump is created by the setting host dumping attrib

write_to_file

bool – When set True data and summaries (as configured) will be written interactively to file during the processing Set during instantiation by the host based on its dump_to_file attrib

dump_file_ext

str – Default file extension for any file names that are auto generated

fname_base

str – First part of any auto generated file names. This is usually overridden in the subclass

dump_summary

bool – If True a summary is recorded each time a new item is added to the the dump. Default is True

summary_sep

str – delimiter for the summary file. default is a space

data_sep

str – delimiter for the data files (arrays saved to file). default is a space

summary_file

str – File path for summary file. Automatically generated. Can be set specifically

create_dump_dir()[source]

Checks dump directory exists, creates it if not

level
The level of data dumping that will occur
• SUMMARY : A summary will be recorded
• FULL : All possible dumping
• 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 what specifically is dumped

class OptimDump(optim, level='SUMMARY')[source]

A container for dumps of optimisation data generated during the pulse optimisation.

dump_summary

bool – When True summary items are appended to the iter_summary

iter_summary

list of optimizer.OptimIterSummary – Summary at each iteration

dump_fid_err

bool – When True values are appended to the fid_err_log

fid_err_log

list of float – Fidelity error at each call of the fid_err_func

dump_grad_norm

bool – When True values are appended to the fid_err_log

grad_norm_log

list of float – Gradient norm at each call of the grad_norm_log

dump_grad

bool – When True values are appended to the grad_log

grad_log

list of ndarray – Gradients at each call of the fid_grad_func

add_iter_summary()[source]

add copy of current optimizer iteration summary

dump_all

True if everything (ignoring the summary) is to be dumped

dump_any

True if anything other than the summary is to be dumped

update_fid_err_log(fid_err)[source]

add an entry to the fid_err log

update_grad_log(grad)[source]

add an entry to the grad log

update_grad_norm_log(grad_norm)[source]

add an entry to the grad_norm log

writeout(f=None)[source]

write all the logs and the summary out to file(s)

Parameters: f (filename or filehandle) – If specified then all summary and object data will go in one file. If None is specified then type specific files will be generated in the dump_dir If a filehandle is specified then it must be a byte mode file as numpy.savetxt is used, and requires this.
class DynamicsDump(dynamics, level='SUMMARY')[source]

A container for dumps of dynamics data. Mainly time evolution calculations

dump_summary

bool – If True a summary is recorded

evo_summary

list of :class:tslotcomp.EvoCompSummary’ – Summary items are appended if dump_summary is True at each recomputation of the evolution.

dump_amps

bool – If True control amplitudes are dumped

dump_dyn_gen

bool – If True the dynamics generators (Hamiltonians) are dumped

dump_prop

bool – If True propagators are dumped

dump_prop_grad

bool – If True propagator gradients are dumped

dump_fwd_evo

bool – If True forward evolution operators are dumped

dump_onwd_evo

bool – If True onward evolution operators are dumped

dump_onto_evo

bool – If True onto (or backward) evolution operators are dumped

evo_dumps

list of EvoCompDumpItem – A new dump item is appended at each recomputation of the evolution. That is if any of the calculation objects are to be dumped.

add_evo_comp_summary(dump_item_idx=None)[source]

add copy of current evo comp summary

add_evo_dump()[source]

Add dump of current time evolution generating objects

dump_all

True if all of the calculation objects are to be dumped

dump_any

True if any of the calculation objects are to be dumped

writeout(f=None)[source]

write all the dump items and the summary out to file(s) :param f: If specified then all summary and object data will go in one file.

If None is specified then type specific files will be generated in the dump_dir If a filehandle is specified then it must be a byte mode file as numpy.savetxt is used, and requires this.
class DumpItem[source]

An item in a dump list

class EvoCompDumpItem(dump)[source]

A copy of all objects generated to calculate one time evolution Note the attributes are only set if the corresponding DynamicsDump dump_ attribute is set.

writeout(f=None)[source]

write all the objects out to files

Parameters: f (filename or filehandle) – If specified then all object data will go in one file. If None is specified then type specific files will be generated in the dump_dir If a filehandle is specified then it must be a byte mode file as numpy.savetxt is used, and requires this.
class DumpSummaryItem`[source]

A summary of the most recent iteration Abstract class only

Attributes: idx : int

Index in the summary list in which this is stored