Efficient extraction of quantum Hamiltonians from optimal laboratory data

Abstract

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.