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] add GRAPE

Usage Example

using GRAPEusing QuantumPropagators: hamiltonian# data structure for `H = H₀ + ϵ(t) H₁`using QuantumControl.Functionals: J_T_sm# square-modulus functionalusing QuantumPropagators: ExpProp# propagation method: matrix exponentiationϵ(t)=0.2# guess pulseH=hamiltonian([10;0-1], ([01;10], ϵ))# time-dependent Hamiltonianket_0, ket_1= ComplexF64[1,0], ComplexF64[0,1]# basis states |0⟩, |1⟩tlist=collect(range(0,5, length=501));# time grid; final time T = 5.0# Optimization functionals depend on states |Ψ(T)⟩, described by a "trajectory"traj= GRAPE.Trajectory(    initial_state= ket_0,    generator= H,    target_state= ket_1);result= GRAPE.optimize(    [traj], tlist;    prop_method= ExpProp,# suitable for small systems only!    J_T= J_T_sm,#  J_T = 1 - |⟨Ψ(T)|1⟩|²# without convergence check, stop after 5000 iterations    check_convergence=(res-> ((res.J_T<1e-3)&&"J_T < 10⁻³")),)ϵ_opt= result.optimized_controls[1]# Or, using the QuantumControl API (recommended)using QuantumControl: ControlProblem, optimize,@optimize_or_loadproblem=ControlProblem(    [traj], tlist,    prop_method= ExpProp,    J_T= J_T_sm,    check_convergence=(res-> ((res.J_T<1e-3)&&"J_T < 10⁻³")),)result=optimize(problem; method=GRAPE)# This dumps the optimization result in `tls_opt.jld2`result=@optimize_or_load("tls_opt.jld2", problem; method= GRAPE)

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