Rejection and Particle Filtering for Hamiltonian Learning

          @misc{ scivideos_PIRSA:16080003,
            doi = {10.48660/16080003},
            url = {https://pirsa.org/16080003},
            author = {Granade, Christopher},
            keywords = {Quantum Matter},
            language = {en},
            title = {Rejection and Particle Filtering for Hamiltonian Learning},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2016},
            month = {aug},
            note = {PIRSA:16080003 see, \url{https://scivideos.org/index.php/pirsa/16080003}}
          }
          

Abstract

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. More information on this topic is available at http://www.cgranade.com/research/talks/qml/2016/