  Optimal Identification (OI) is a recently developed procedure for extracting
optimal information about quantum Hamiltonians from experimental data using
shaped control fields to drive the system in such a manner that dynamical
measurements provide maximal information about its Hamiltonian. However, while
optimal, OI is computationally expensive as initially presented. Here, we
describe the unification of OI with highly efficient global, nonlinear
map-facilitated data inversion procedures. This combination is expected to make
OI techniques more suitable for laboratory implementation. A simulation of
map-facilitated OI is performed demonstrating that the input-output maps can
greatly accelerate the inversion process.
