Types

ErrorSource

Represents a source of error in the Hamiltonian.

Fields

• Herror::Function: A function that returns the error Hamiltonian matrix. Signature: Herror(time_step::Int, x::Vector{<:Real}, x_add::Vector{<:Real}, err::Real). x has for size the number of main parameters, x_add has for size the number of additional parameters. time_step is an integer between 1 and ntimes.

UnitaryRobustGRAPEProblem

Represents a robust GRAPE unitary calculation problem.

Fields

• t0::Real: Total evolution time
• ntimes::Int: Number of time steps
• ndim::Int: Dimension of the Hilbert space
• H0::Function: Main Hamiltonian function (must return a matrix). Signature: H0(time_step::Int, x::Vector{<:Real}, x_add::Vector{<:Real}). x has for size the number of main parameters, x_add has for size the number of additional parameters. time_step is an integer between 1 and ntimes.
• nb_additional_param::Int: Number of additional parameters
• error_sources::Vector{ErrorSource}: List of error sources
• ϵ::Real = 1e-8: Small parameter for first-order finite difference
• ϵ2::Real = 1e-4: Small parameter for second-order finite difference

FidelityRobustGRAPEProblem

Represents a robust GRAPE fidelity calculation problem.

Fields

• unitary_problem::UnitaryRobustGRAPEProblem: The underlying optimization problem
• projector::Matrix{<:Real}: Projector for subspace fidelity. It can be a "pseudo"-projector (see Quick start and example for more information)
• target_unitary::Function: Function that returns the target unitary. Signature: target_unitary(x_add::Vector{<:Real}) where x_add has size the number of additional parameters.

FidelityRobustGRAPEParameters

Configuration parameters for parameterized quantum gate optimization according to the GRAPE algorithm with optional robustness.

Fields

• x_initial::Vector{<:Real}: Initial control pulse amplitudes and additional parameters
• regularization_functions::Vector{Function}: Vector of functions to regularize the control pulses. The vector has size the number of main parameters. Each function has signature (x::Vector{<:Real}) -> (r1::Real,j1::Vector{<:Real},r2::Real,j2::Vector{<:Real}) where r1 and r2 are the first and second-order costs, and j1 and j2 are the gradients of these costs w.r.t. the control parameters. x, j1 and j2 have size ntimes.
• regularization_coeff1::Vector{<:Real}: First-order regularization coefficients; has size the number of main parameters.
• regularization_coeff2::Vector{<:Real}: Second-order regularization coefficients; has size the number of main parameters.
• error_source_coeff::Vector{<:Real}: Coefficients for each error source. Has size the number of error sources.
• time_limit::Real: Maximum time for optimization in seconds (NaN means no limit)
• iterations::Int: Maximum number of optimization iterations
• solver_algorithm::Optim.FirstOrderOptimizer: Optimization algorithm (e.g., LBFGS(), GradientDescent())
• additional_parameters::Dict{Symbol,Any}: Additional parameters to pass to Optim.optimize