Usage

Solve a quantum control problem using the GRAPE method.

using GRAPE
result = GRAPE.optimize(trajectories, tlist; J_T, kwargs...)

minimizes a functional

\[J(\{ϵ_{nl}\}) = J_T(\{|Ψ_k(T)⟩\}) + λ_a J_a(\{ϵ_{nl}\})\,,\]

via the GRAPE method, where the final time functional $J_T$ depends explicitly on the forward-propagated states $|Ψ_k(T)⟩$, where $|Ψ_k(t)⟩$ is the time evolution of the initial_state in the $k$th' element of the trajectories, and the running cost $J_a$ depends explicitly on pulse values $ϵ_{nl}$ of the l'th control discretized on the n'th interval of the time grid tlist.

It does this by calculating the gradient of the final-time functional

\[\nabla J_T \equiv \frac{\partial J_T}{\partial ϵ_{nl}} = -2 \Re \underbrace{% \underbrace{\bigg\langle χ(T) \bigg\vert \hat{U}^{(k)}_{N_T} \dots \hat{U}^{(k)}_{n+1} \bigg \vert}_{\equiv \bra{\chi(t_n)}\;\text{(bw. prop.)}} \frac{\partial \hat{U}^{(k)}_n}{\partial ϵ_{nl}} }_{\equiv \bra{χ_k^\prime(t_{n-1})}} \underbrace{\bigg \vert \hat{U}^{(k)}_{n-1} \dots \hat{U}^{(k)}_1 \bigg\vert Ψ_k(t=0) \bigg\rangle}_{\equiv |\Psi(t_{n-1})⟩\;\text{(fw. prop.)}}\,,\]

where $\hat{U}^{(k)}_n$ is the time evolution operator for the $n$ the interval, generally assumed to be $\hat{U}^{(k)}_n = \exp[-i \hat{H}_{kn} dt_n]$, where $\hat{H}_{kn}$ is the operator obtained by evaluating trajectories[k].generator on the $n$'th time interval.

The backward-propagation of $|\chi_k(t)⟩$ has the boundary condition

\[ |\chi_k(T)⟩ \equiv - \frac{\partial J_T}{\partial ⟨\Psi_k(T)|}\,.\]

The final-time gradient $\nabla J_T$ is combined with the gradient for the running costs, and the total gradient is then fed into an optimizer (L-BFGS-B by default) that iteratively changes the values $\{ϵ_{nl}\}$ to minimize $J$.

See Background for details.

Returns a GrapeResult.

Positional arguments

• trajectories: A vector of Trajectory objects. Each trajectory contains an initial_state and a dynamical generator (e.g., time-dependent Hamiltonian). Each trajectory may also contain arbitrary additional attributes like target_state to be used in the J_T functional
• tlist: A vector of time grid values.

Required keyword arguments

• J_T: A function J_T(Ψ, trajectories) that evaluates the final time functional from a list Ψ of forward-propagated states and trajectories. The function J_T may also take a keyword argument tau. If it does, a vector containing the complex overlaps of the target states (target_state property of each trajectory in trajectories) with the propagated states will be passed to J_T.

Optional keyword arguments

• chi: A function chi(Ψ, trajectories) that receives a list Ψ of the forward propagated states and returns a vector of states $|χₖ⟩ = -∂J_T/∂⟨Ψₖ|$. If not given, it will be automatically determined from J_T via QuantumControl.Functionals.make_chi with the default parameters. Similarly to J_T, if chi accepts a keyword argument tau, it will be passed a vector of complex overlaps.
• chi_min_norm=1e-100: The minimum allowable norm for any $|χₖ(T)⟩$. Smaller norms would mean that the gradient is zero, and will abort the optimization with an error.
• J_a: A function J_a(pulsevals, tlist) that evaluates running costs over the pulse values, where pulsevals are the vectorized values $ϵ_{nl}$, where n are in indices of the time intervals and l are the indices over the controls, i.e., [ϵ₁₁, ϵ₂₁, …, ϵ₁₂, ϵ₂₂, …] (the pulse values for each control are contiguous). If not given, the optimization will not include a running cost.
• gradient_method=:gradgen: One of :gradgen (default) or :taylor. With gradient_method=:gradgen, the gradient is calculated using QuantumGradientGenerators. With gradient_method=:taylor, it is evaluated via a Taylor series, see Eq. (20) in Kuprov and Rogers, J. Chem. Phys. 131, 234108 (2009) [22].
• taylor_grad_max_order=100: If given with gradient_method=:taylor, the maximum number of terms in the Taylor series. If taylor_grad_check_convergence=true (default), if the Taylor series does not convergence within the given number of terms, throw an an error. With taylor_grad_check_convergence=true, this is the exact order of the Taylor series.
• taylor_grad_tolerance=1e-16: If given with gradient_method=:taylor and taylor_grad_check_convergence=true, stop the Taylor series when the norm of the term falls below the given tolerance. Ignored if taylor_grad_check_convergence=false.
• taylor_grad_check_convergence=true: If given as true (default), check the convergence after each term in the Taylor series an stop as soon as the norm of the term drops below the given number. If false, stop after exactly taylor_grad_max_order terms.
• lambda_a=1: A weight for the running cost J_a.
• grad_J_a: A function to calculate the gradient of J_a. If not given, it will be automatically determined. See make_grad_J_a for the required interface.
• upper_bound: An upper bound for the value of any optimized control. Time-dependent upper bounds can be specified via pulse_options.
• lower_bound: A lower bound for the value of any optimized control. Time-dependent lower bounds can be specified via pulse_options.
• pulse_options: A dictionary that maps every control (as obtained by get_controls from the trajectories) to a dict with the following possible keys:
  • :upper_bounds: A vector of upper bound values, one for each intervals of the time grid. Values of Inf indicate an unconstrained upper bound for that time interval, respectively the global upper_bound, if given.
  • :lower_bounds: A vector of lower bound values. Values of -Inf indicate an unconstrained lower bound for that time interval,
• callback: A function that receives the GRAPE workspace and the iteration number. The function may return a tuple of values which are stored in the GrapeResult object result.records. The function can also mutate the workspace, in particular the updated pulsevals. This may be used, e.g., to apply a spectral filter to the updated pulses or to perform similar manipulations.
• check_convergence: A function to check whether convergence has been reached. Receives a GrapeResult object result, and must return one of the following:
  • A boolean (true if convergence is reached, false otherwise)
  • A string with a reason for the convergence, or an empty string if not converged.
  • The original result object or nothing, indicating that result.converged and result.message may have been modified to indicate convergence
  The convergence check is performed after any callback.
• prop_method: The propagation method to use for each trajectory, see below.
• verbose=false: If true, print information during initialization
• rethrow_exceptions: By default, any exception ends the optimization, but still returns a GrapeResult 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. If rethrow_exceptions=true, instead of capturing the exception, it will be thrown normally.

Experimental keyword arguments

The following keyword arguments may change in non-breaking releases:

• x_tol: Parameter for Optim.jl
• f_tol: Parameter for Optim.jl
• g_tol: Parameter for Optim.jl
• show_trace: Parameter for Optim.jl
• extended_trace: Parameter for Optim.jl
• show_every: Parameter for Optim.jl
• allow_f_increases: Parameter for Optim.jl
• optimizer: An optional Optim.jl optimizer (Optim.AbstractOptimizer instance). If not given, an L-BFGS-B optimizer will be used.

Trajectory propagation

GRAPE may involve three types of time propagation, all of which are implemented via the QuantumPropagators as a numerical backend:

• A forward propagation for every Trajectory in the trajectories
• A backward propagation for every trajectory
• A backward propagation of a gradient generator for every trajectory.

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_ prefix can be used, respectively. These override any setting with the prop_ prefix. Similarly, properties for the backward propagation of the gradient generators can be set with properties that have a grad_prop_ prefix. These prefixes apply both to the properties of each Trajectory and the 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 keyword argument.

using GRAPE
result = optimize(problem; method=GRAPE, kwargs...)

optimizes the given QuantumControl.ControlProblem using the GRAPE (Gradient-Ascent Pulse Engineering) method.

Delegates to

result = GRAPE.optimize(
    problem.trajectories, problem.tlist; problem.kwargs..., kwargs...
)

See GRAPE.optimize for details and supported keyword arguments.

Compared to calling GRAPE.optimize directly, the QuantumControl.optimize wrapper adds the following additional keyword arguments:

• check=true: If true (default), test that all the objects stored in the trajectories implement the required interfaces correctly
• print_iters=true: Whether to print information after each iteration.
• print_iter_info=["iter.", "J_T", "|∇J|", "|Δϵ|", "ΔJ", "FG(F)", "secs"]: Which fields to print if print_iters=true. See make_grape_print_iters
• store_iter_info=[]: Which fields to store in result.records, given as a list of header labels, see print_iter_info. See make_grape_print_iters

These options still allow for the normal callback argument. With QuantumcControl.optimize, the callback can be a tuple of callback functions that will be combined automatically, which GRAPE.optimize only supports as single callback function.

The GRAPE optimization may also be initiated via QuantumControl.@optimize_or_load, which additionally adds checkpointing, to ensure that an optimization result is dumped to disk in case of an unexpected shutdown.