Rejection and Particle Filtering for Hamiltonian Learning

QuICS Special Seminar

Christopher Granade (University of Sydney)
Thursday, July 21, 2016 - 11:00am
CSS 3100A

Many tasks in quantum information rely on accurate knowledge of a system's Hamiltonian, including calibrating control, characterizing devices, and verifying quantum simulators. In this talk, we pose the problem of learning Hamiltonians as an instance of parameter estimation. We then solve this problem with Bayesian inference, and describe how rejection and particle filtering provide efficient numerical algorithms for learning Hamiltonians. Finally, we discuss how filtering can be combined with quantum resources to verify quantum systems beyond the reach of classical simulators.