From 5671347501e126817da908bc79eb0982ae00731e Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Wed, 7 Feb 2024 20:04:48 +0000 Subject: [PATCH] build based on 36b15d5 --- dev/index.html | 2 +- dev/search/index.html | 2 +- dev/search_index.js | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/dev/index.html b/dev/index.html index 8ded4ad..b11a6fe 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,2 +1,2 @@ -HermiteOptimalControl.jl Documentation · HermiteOptimalControl.jl
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HermiteOptimalControl.jl Documentation

Workflow

Right now the user can set up Schrodinger problems, and compute the gradients of them with the control vector and target gate of their choosing. In a complete package, there would also be an optimization procedure which uses this gradient calculation.

The basic workflow is:

  1. Set up a SchrodingerProblem, either yourself using the constructor SchrodingerProb, or by using one of the example problems provided. Schrodinger Problem Examples
  2. Choose a control vector and target.
  3. Compute a gradient using one of the methods provided. Gradient Evaluation

Functions

Schrodinger Problem Definition

SchrodingerProb

Schrodinger Problem Examples

Missing docstring.

Missing docstring for rabi_osc. Check Documenter's build log for details.

Missing docstring.

Missing docstring for gargamel_prob. Check Documenter's build log for details.

Missing docstring.

Missing docstring for bspline_prob. Check Documenter's build log for details.

Forward Evolution

Functions for evolving the state vector in a problem forward in time according to Schrodinger's equation, with or without forcing.

Missing docstring.

Missing docstring for eval_forward_forced. Check Documenter's build log for details.

Gradient Evaluation

HermiteOptimalControl.discrete_adjoint — Function

Evaluates gradient of the provided Schrodinger problem with the given target gate and control parameter(s) pcof using the discrete adjoint method.

Returns: gradient

source
HermiteOptimalControl.eval_grad_forced — Function

Evaluates gradient of the provided Schrodinger problem with the given target gate and control parameter(s) pcof using the "forward differentiation" method, which evolves a differentiated Schrodinger equation, using the state vector in the evolution of the original Schrodinger equation as a forcing term.

Returns: gradient

source
HermiteOptimalControl.eval_grad_finite_difference — Function

Vector control version.

Evaluates gradient of the provided Schrodinger problem with the given target gate and control parameter(s) pcof using a finite difference method, where a step size of dpcof is used when perturbing the components of the control vector pcof.

Returns: gradient

source
HermiteOptimalControl.infidelity — Function

Calculates the infidelity for the given state vector 'ψ' and target state 'target.'

Returns: Infidelity

source

Calculates the infidelity for the given matrix of state vectors 'Q' and matrix of target states 'target.'

Returns: Infidelity

source

Bsplines

HermiteOptimalControl.bcparams — Type
bcpar = bcparams(T, D1, Ncoupled, Nunc, omega, pcof)

General constructor of struct bcparams for setting up B-splines with carrier waves.

bcpar = bcparams(T, D1, omega, pcof)

Simplified constructor for the case when there are no uncoupled controls and Ncoupled = size(omega,1).

Arguments

  • T:: Float64: Duration of spline function
  • D1:: Int64: Number of basis functions in each segment
  • Ncoupled::Int64: Number of coupled controls in the simulation
  • Nunc::Int64: Number of uncoupled controls in the simulation
  • omega::Array{Float64,2}: Carrier wave frequencies
  • pcof:: Array{Float64, 1}: Coefficient vector. Must have D1*Nseg elements

First dimensions of the omega array:

  • Without uncoupled controls, Nunc=0 and size(omega,1) = Ncoupled.
  • With uncoupled controls, Nunc > 0 and size(omega,1) = Ncoupled + Nunc.

Second dimension of the omega array:

  • size(omega, 2) = Nfreq

Ordering of the pcof array:

First consider the case without uncoupled control functions, Nunc = 0: Then the pcof array then has 2*Ncoupled*Nfreq*D1 elements. Each ctrl ∈ [1,Ncoupled] and freq ∈ [1,Nfreq] corresponds to D1 elements in the pcof vector. For the case Ncoupled = 2 and Nfreq = 2, the elements are ordered according to

ctrlfreqα_1α_2
111:D1D1+1:2 D1
122 D1+1: 3 D13 D1+1:4 D1
214 D1+1: 5 D15 D1+1:6 D1
226 D1+1: 7 D17 D1+1: 8D1

If there are uncoupled controls, Nunc > 0, the pcof array should have (2*Ncoupled + Nunc)*Nfreq*D1 elements. The last Nunc*Nfreq*D1 elements correspond to the uncoupled control functions and are ordered in a corresponding way.

External links

source
HermiteOptimalControl.bcarrier2 — Function
f = bcarrier2(t, params, func)

Evaluate a B-spline function with carrier waves. See also the bcparams constructor.

Arguments

  • t::Float64: Evaluate spline at parameter t ∈ [0, param.T]
  • param::params: Parameters for the spline
  • func::Int64: Spline function index ∈ [0, param.Nseg-1]
source
f = bcarrier2(t, params, func)

Evaluate a B-spline function with carrier waves. See also the bcparams constructor.

Arguments

  • t::Float64: Evaluate spline at parameter t ∈ [0, param.T]
  • param::params: Parameters for the spline
  • func::Int64: Spline function index ∈ [0, param.Nseg-1]
source
HermiteOptimalControl.bcarrier2_dt — Function
f = bcarrier2_dt(t, params, func)

Evaluate a B-spline function with carrier waves. See also the bcparams constructor.

Arguments

  • t::Float64: Evaluate spline at parameter t ∈ [0, param.T]
  • param::params: Parameters for the spline
  • func::Int64: Spline function index ∈ [0, param.Nseg-1]

SPENCER: To get cross derivative (d2/dtda, just remove the pcofs in the below, since the controls are linear in them)

source
f = bcarrier2_dt(t, params, func)

Evaluate a B-spline function with carrier waves. See also the bcparams constructor.

Arguments

  • t::Float64: Evaluate spline at parameter t ∈ [0, param.T]
  • param::params: Parameters for the spline
  • func::Int64: Spline function index ∈ [0, param.Nseg-1]

SPENCER: To get cross derivative (d2/dtda, just remove the pcofs in the below, since the controls are linear in them)

source
HermiteOptimalControl.gradbcarrier2! — Function
gradbcarrier2!(t, bcpar, func, g) -> g

Evaluate the gradient of a control function with respect to all coefficient.

NOTE: the index of the control functions is 0-based. For a set of coupled controls, mod(func,2)=0 corresponds to ∇ pj(t) and mod(func,2) = 1 corresponds to ∇ qj(t), where j = div(func,2).

Arguments

  • t::Float64: Evaluate spline at parameter t ∈ [0, param.T]
  • bcpar::bcparams: Parameters for the spline
  • func::Int64: Control function index ∈ [0, param.Nseg-1]
  • g::Array{Float64,1}: Preallocated array to store calculated gradient
source
Missing docstring.

Missing docstring for gradbcarrier2_dt!. Check Documenter's build log for details.

+HermiteOptimalControl.jl Documentation · HermiteOptimalControl.jl
Edit on GitHub

HermiteOptimalControl.jl Documentation

Workflow

Right now the user can set up Schrodinger problems, and compute the gradients of them with the control vector and target gate of their choosing. In a complete package, there would also be an optimization procedure which uses this gradient calculation.

The basic workflow is:

  1. Set up a SchrodingerProblem, either yourself using the constructor SchrodingerProb, or by using one of the example problems provided. Schrodinger Problem Examples
  2. Choose a control vector and target.
  3. Compute a gradient using one of the methods provided. Gradient Evaluation

Functions

Schrodinger Problem Definition

SchrodingerProb

Schrodinger Problem Examples

Missing docstring.

Missing docstring for rabi_osc. Check Documenter's build log for details.

Missing docstring.

Missing docstring for gargamel_prob. Check Documenter's build log for details.

Missing docstring.

Missing docstring for bspline_prob. Check Documenter's build log for details.

Forward Evolution

Functions for evolving the state vector in a problem forward in time according to Schrodinger's equation, with or without forcing.

HermiteOptimalControl.eval_forward — Function

Evolve a vector SchrodingerProblem forward in time. Return the history of the state vector (u/v) in a 3-index array, where the first index of corresponds to the vector component, the second index corresponds to the derivative to be taken, and the third index corresponds to the timestep number.

source
Missing docstring.

Missing docstring for eval_forward_forced. Check Documenter's build log for details.

Gradient Evaluation

Missing docstring.

Missing docstring for eval_grad_forced. Check Documenter's build log for details.

Missing docstring.

Missing docstring for eval_grad_finite_difference. Check Documenter's build log for details.

HermiteOptimalControl.infidelity — Function

Calculates the infidelity for the given state vector 'ψ' and target state 'target.'

Returns: Infidelity

source

Calculates the infidelity for the given matrix of state vectors 'Q' and matrix of target states 'target.'

Returns: Infidelity

source

Bsplines

Missing docstring.

Missing docstring for bcparams. Check Documenter's build log for details.

Missing docstring.

Missing docstring for bcarrier2. Check Documenter's build log for details.

Missing docstring.

Missing docstring for bcarrier2_dt. Check Documenter's build log for details.

Missing docstring.

Missing docstring for gradbcarrier2!. Check Documenter's build log for details.

Missing docstring.

Missing docstring for gradbcarrier2_dt!. Check Documenter's build log for details.

diff --git a/dev/search/index.html b/dev/search/index.html index 579efca..650b656 100644 --- a/dev/search/index.html +++ b/dev/search/index.html @@ -1,2 +1,2 @@ -Search · HermiteOptimalControl.jl

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    +Search · HermiteOptimalControl.jl

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      diff --git a/dev/search_index.js b/dev/search_index.js index 9f3482b..815a5de 100644 --- a/dev/search_index.js +++ b/dev/search_index.js @@ -1,3 +1,3 @@ var documenterSearchIndex = {"docs": -[{"location":"#HermiteOptimalControl.jl-Documentation","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.jl Documentation","text":"","category":"section"},{"location":"","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.jl Documentation","text":"","category":"page"},{"location":"#Workflow","page":"HermiteOptimalControl.jl Documentation","title":"Workflow","text":"","category":"section"},{"location":"","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.jl Documentation","text":"Right now the user can set up Schrodinger problems, and compute the gradients of them with the control vector and target gate of their choosing. In a complete package, there would also be an optimization procedure which uses this gradient calculation.","category":"page"},{"location":"","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.jl Documentation","text":"The basic workflow is:","category":"page"},{"location":"","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.jl Documentation","text":"Set up a SchrodingerProblem, either yourself using the constructor SchrodingerProb, or by using one of the example problems provided. Schrodinger Problem Examples\nChoose a control vector and target.\nCompute a gradient using one of the methods provided. Gradient Evaluation","category":"page"},{"location":"#Functions","page":"HermiteOptimalControl.jl Documentation","title":"Functions","text":"","category":"section"},{"location":"#Schrodinger-Problem-Definition","page":"HermiteOptimalControl.jl Documentation","title":"Schrodinger Problem Definition","text":"","category":"section"},{"location":"","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.jl Documentation","text":"SchrodingerProb","category":"page"},{"location":"#Schrodinger-Problem-Examples","page":"HermiteOptimalControl.jl Documentation","title":"Schrodinger Problem Examples","text":"","category":"section"},{"location":"","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.jl Documentation","text":"rabi_osc\ngargamel_prob\nbspline_prob","category":"page"},{"location":"#Forward-Evolution","page":"HermiteOptimalControl.jl Documentation","title":"Forward Evolution","text":"","category":"section"},{"location":"","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.jl Documentation","text":"Functions for evolving the state vector in a problem forward in time according to Schrodinger's equation, with or without forcing.","category":"page"},{"location":"","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.jl Documentation","text":"eval_forward\neval_forward_forced","category":"page"},{"location":"#HermiteOptimalControl.eval_forward","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.eval_forward","text":"\n\n\n\n","category":"function"},{"location":"#Gradient-Evaluation","page":"HermiteOptimalControl.jl Documentation","title":"Gradient Evaluation","text":"","category":"section"},{"location":"","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.jl Documentation","text":"discrete_adjoint\neval_grad_forced\neval_grad_finite_difference\ninfidelity","category":"page"},{"location":"#HermiteOptimalControl.discrete_adjoint","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.discrete_adjoint","text":"Evaluates gradient of the provided Schrodinger problem with the given target gate and control parameter(s) pcof using the discrete adjoint method. \n\nReturns: gradient\n\n\n\n\n\n","category":"function"},{"location":"#HermiteOptimalControl.eval_grad_forced","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.eval_grad_forced","text":"Evaluates gradient of the provided Schrodinger problem with the given target gate and control parameter(s) pcof using the \"forward differentiation\" method, which evolves a differentiated Schrodinger equation, using the state vector in the evolution of the original Schrodinger equation as a forcing term.\n\nReturns: gradient\n\n\n\n\n\n","category":"function"},{"location":"#HermiteOptimalControl.eval_grad_finite_difference","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.eval_grad_finite_difference","text":"Vector control version.\n\nEvaluates gradient of the provided Schrodinger problem with the given target gate and control parameter(s) pcof using a finite difference method, where a step size of dpcof is used when perturbing the components of the control vector pcof.\n\nReturns: gradient\n\n\n\n\n\n","category":"function"},{"location":"#HermiteOptimalControl.infidelity","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.infidelity","text":"Calculates the infidelity for the given state vector 'ψ' and target state 'target.'\n\nReturns: Infidelity\n\n\n\n\n\nCalculates the infidelity for the given matrix of state vectors 'Q' and matrix of target states 'target.'\n\nReturns: Infidelity\n\n\n\n\n\n","category":"function"},{"location":"#Bsplines","page":"HermiteOptimalControl.jl Documentation","title":"Bsplines","text":"","category":"section"},{"location":"","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.jl Documentation","text":"bcparams\nbcarrier2\nbcarrier2_dt\ngradbcarrier2!\ngradbcarrier2_dt!","category":"page"},{"location":"#HermiteOptimalControl.bcparams","page":"HermiteOptimalControl.jl Documentation","title":"HermiteOptimalControl.bcparams","text":"bcpar = bcparams(T, D1, Ncoupled, Nunc, omega, pcof)\n\nGeneral constructor of struct bcparams for setting up B-splines with carrier waves.\n\nbcpar = bcparams(T, D1, omega, pcof)\n\nSimplified constructor for the case when there are no uncoupled controls and Ncoupled = size(omega,1).\n\nArguments\n\nT:: Float64: Duration of spline function\nD1:: Int64: Number of basis functions in each segment\nNcoupled::Int64: Number of coupled controls in the simulation\nNunc::Int64: Number of uncoupled controls in the simulation\nomega::Array{Float64,2}: Carrier wave frequencies\npcof:: Array{Float64, 1}: Coefficient vector. Must have D1*Nseg elements\n\nFirst dimensions of the omega array:\n\nWithout uncoupled controls, Nunc=0 and size(omega,1) = Ncoupled.\nWith uncoupled controls, Nunc > 0 and size(omega,1) = Ncoupled + Nunc.\n\nSecond dimension of the omega array:\n\nsize(omega, 2) = Nfreq\n\nOrdering of the pcof array:\n\nFirst consider the case without uncoupled control functions, Nunc = 0: Then the pcof array then has 2*Ncoupled*Nfreq*D1 elements. Each ctrl ∈ [1,Ncoupled] and freq ∈ [1,Nfreq] corresponds to D1 elements in the pcof vector. For the case Ncoupled = 2 and Nfreq = 2, the elements are ordered according to\n\nctrl freq α_1 α_2\n1 1 1:D1 D1+1:2 D1\n1 2 2 D1+1: 3 D1 3 D1+1:4 D1\n2 1 4 D1+1: 5 D1 5 D1+1:6 D1\n2 2 6 D1+1: 7 D1 7 D1+1: 8D1\n\nIf there are uncoupled controls, Nunc > 0, the pcof array should have (2*Ncoupled + Nunc)*Nfreq*D1 elements. 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