Optimization

optimize_gate(schro_prob, controls, pcof_init, target, [order=4, pcof_L=missing, pcof_U=missing, maxIter=50, print_level=5, ridge_penalty_strength=1e-2, max_cpu_time = 300.0])

Perform gradient-based search (L-BFGS) to find value of the control vector pcof which minimizes the objective function for the given problem and target. Returns a dictionary which contains the ipopt optimization problem object, as well as other information about the optimization.

NOTE: to play around with IPOPT settings which are not accessible through this function call, could run the optimization with maxIter=1, then grab the IPOPT problem from the return dictionary, and change the IPOPT settings directly through the IPOPT API.

Arguments

• prob::SchrodingerProb: Object containing the Hamiltonians, number of timesteps, etc.
• controls: An AstractControl or vector of controls, where the i-th control corresponds to the i-th control Hamiltonian.
• pcof::AbstractVector{<: Real}: The control vector.
• target::AbstractMatrix{Float64}: The target gate, in 'stacked' real-valued format.
• order::Int64=2: Which order of the timestepping method to use.
• pcof_L=missing: Lower bounds of the control parameters. Can either be a single number, used for all parameters, or a vector the same length as pcof, which will set a lower limit on each parameter.
• pcof_U=missing: Upper bounds of the control parameters.
• maxIter=50: Maximum number of iterations to perform.
• print_level=5: Print level of IPOPT.
• ridge_penalty_strength: Strength of the ridge/Tikhonov regularization term in the objective function.
• max_cpu_time: Maximum CPU time (in seconds) to spend on the optimization problem.