Efficient extraction of quantum Hamiltonians from optimal laboratory data

Optimal identification (OI) is a recently developed procedure for extracting information about quantum Hamiltonians from experimental data. It employs techniques from coherent learning control to drive the quantum system such that dynamical measurements provide maximal information about its Hamiltonian. OI is an optimal procedure as initially presented; however, the data inversion component is computationally expensive. Here, we demonstrate that highly efficient global, nonlinear, map-facilitated inversion procedures can be combined with the OI concept to make it more suitable for laboratory implementation. A simulation of map-facilitated OI illustrates how the input-output maps can greatly accelerate the data inversion process.