API
Krotov.KrotovResult
— TypeResult object returned by optimize_krotov
.
Attributes
The attributes of a KrotovResult
object include
iter
: The number of the current iterationJ_T
: The value of the final-time functional in the current iterationJ_T_prev
: The value of the final-time functional in the previous iterationtlist
: The time grid on which the control are discretized.guess_controls
: A vector of the original control fields (each field discretized to the points oftlist
)optimized_controls
: A vector of the optimized control fields. Calculated only at the end of the optimization, not after each iteration.tau_vals
: For any trajectory that defines atarget_state
, the complex overlap of that target state with the propagated state. For any trajectory for which thetarget_state
isnothing
, the value is zero.records
: A vector of tuples with values returned by acallback
routine passed tooptimize
converged
: A boolean flag on whether the optimization is converged. This may be set totrue
by acheck_convergence
function.message
: A message string to explain the reason for convergence. This may be set by acheck_convergence
function.
All of the above attributes may be referenced in a check_convergence
function passed to optimize(problem; method=Krotov)
Krotov.KrotovWrk
— TypeKrotov workspace.
The workspace is for internal use. However, it is also accessible in a callback
function. The callback may use or modify some of the following attributes:
trajectories
: a copy of the trajectories defining the control problemadjoint_trajectories
: Thetrajectories
with the adjoint generatorkwargs
: The keyword arguments from theControlProblem
or the call tooptimize
.controls
: A tuple of the original controls (probably functions)ga_a_int
: The current value of $∫gₐ(t)dt$ for each controlupdate_shapes
: The update shapes $S(t)$ for each pulse, discretized on the intervals of the time grid.lambda_vals
: The current value of λₐ for each controlresult
: The current result objectfw_storage
: The storage of states for the forward propagationfw_propagators
: The propagators used for the forward propagationbw_propagators
: The propagators used for the backward propagationuse_threads
: Flag indicating whether the propagations are performed in parallel.
Krotov.optimize_krotov
— MethodQuantumControl.optimize
— Methodusing Krotov
result = optimize(problem; method=Krotov, kwargs...)
optimizes the given control problem
using Krotov's method, by minimizing the functional
\[J(\{ϵ_l(t)\}) = J_T(\{|Ψ_k(T)⟩\}) + ∑_l \int_{0}^{T} \frac{λ_{a,l}}{S_l(t)} [ϵ_l(t) - ϵ_l^{(0)}(t)]^2 \, dt\,,\]
cf. the general form of a quantum control functional. The "reference field" $ϵ_l^{(0)}(t)$ is the guess control for that particular iteration. The above functional implies a first-order update equation
\[Δϵ_l(t) = \frac{S_l(t)}{λ_{a,l}} \Im ∑_k \left[ \Big\langle \chi_k^{(0)}(t) \Big\vert \frac{\partial \hat{H}_k}{\partial ϵ_l(t)} \Big\vert \Psi_k(t) \Big\rangle \right]\,,\]
where $|\chi^{(0)}_k(t)⟩$ is the state backward-propagated under $Ĥ_k^{\dagger}(\{ϵ_l^{(0)}(t)\})$ with the boundary condition $|\chi_k(T)⟩ = \partial J_T / \partial ⟨Ψ_k^{(0)}(T)|$ and $Ĥ_k$ is the generator
of the $k$'th trajectory.
Note that the particular control-dependent running cost in the above functional is required to obtain the given Krotov update equation. Other running costs, or state-dependent running costs are not supported in this implementation of Krotov's method (even though some running costs are mathematically compatible with Krotov's method).
Returns a KrotovResult
.
Keyword arguments that control the optimization are taken from the keyword arguments used in the instantiation of problem
; any of these can be overridden with explicit keyword arguments to optimize
.
Required problem keyword arguments
J_T
: A functionJ_T(Ψ, trajectories)
that evaluates the final time functional from a listΨ
of forward-propagated states andproblem.trajectories
. The functionJ_T
may also take a keyword argumenttau
. If it does, a vector containing the complex overlaps of the target states (target_state
property of each trajectory inproblem.trajectories
) with the propagated states will be passed toJ_T
.
Recommended problem keyword arguments
lambda_a=1.0
: The inverse Krotov step width λₐ for every pulse.update_shape=(t->1.0)
: A functionS(t)
for the "update shape" that scales the update for every pulse.
If different controls require different lambda_a
or update_shape
, a dict pulse_options
must be given instead of a global lambda_a
and update_shape
; see below.
Optional problem keyword arguments
The following keyword arguments are supported (with default values):
pulse_options
: A dictionary that maps every control (as obtained byget_controls
from theproblem.trajectories
) to the following dict::lambda_a
: The value for inverse Krotov step width λₐ.:update_shape
: A functionS(t)
for the "update shape" that scales the Krotov pulse update.
This overrides the global
lambda_a
andupdate_shape
arguments.chi
: A functionchi(Ψ, trajectories)
that receives a listΨ
of the forward propagated states and returns a vector of states $|χₖ⟩ = -∂J_T/∂⟨Ψₖ|$. If not given, it will be automatically determined fromJ_T
viamake_chi
with the default parameters. Similarly toJ_T
, ifchi
accepts a keyword argumenttau
, it will be passed a vector of complex overlaps.sigma=nothing
: A function that calculates the second-order contribution. If not given, the first-order Krotov method is used.iter_start=0
: The initial iteration number.iter_stop=5000
: The maximum iteration number.prop_method
: The propagation method to use for each trajectory; see below.print_iters=true
: Whether to print information after each iteration.store_iter_info=Set()
: Which fields fromprint_iters
to store inresult.records
. A subset ofSet(["iter.", "J_T", "∫gₐ(t)dt", "J", "ΔJ_T", "ΔJ", "secs"])
.callback
: A function (or tuple of functions) that receives the Krotov workspace, the iteration number, the list of updated pulses, and the list of guess pulses as positional arguments. The function may return a tuple of values which are stored in theKrotovResult
objectresult.records
. The function can also mutate any of its arguments, in particular the updated pulses. This may be used, e.g., to apply a spectral filter to the updated pulses or to perform similar manipulations. Note thatprint_iters=true
(default) adds an automatic callback to print information after each iteration. Withstore_iter_info
, that callback automatically stores a subset of the printed information.check_convergence
: A function to check whether convergence has been reached. Receives aKrotovResult
objectresult
, and should setresult.converged
totrue
andresult.message
to an appropriate string in case of convergence. Multiple convergence checks can be performed by chaining functions with∘
. The convergence check is performed after anycallback
.verbose=false
: Iftrue
, print information during initialization.rethrow_exceptions
: By default, any exception ends the optimization but still returns aKrotovResult
that captures the message associated with the exception. This is to avoid losing results from a long-running optimization when an exception occurs in a later iteration. Ifrethrow_exceptions=true
, instead of capturing the exception, it will be thrown normally.
Trajectory propagation
Krotov's method involves the forward and backward propagation for every Trajectory
in the problem
. The keyword arguments for each propagation (see propagate
) are determined from any properties of each Trajectory
that have a prop_
prefix, cf. init_prop_trajectory
.
In situations where different parameters are required for the forward and backward propagation, instead of the prop_
prefix, the fw_prop_
and bw_prop_
prefixes can be used, respectively. These override any setting with the prop_
prefix. This applies both to the properties of each Trajectory
and the problem keyword arguments.
Note that the propagation method for each propagation must be specified. In most cases, it is sufficient (and recommended) to pass a global prop_method
problem keyword argument.