To be presented in the week of 7 September, as a seminar at the University of Sydney.
Recent work in quantum Hamiltonian learning (QHL) has shown that quantum simulation is a valuable tool for learning empirical models for quantum systems.
In this talk, we review the classical and quantum Hamiltonian learning formalisms, then show an extension of QHL that uses small quantum simulators to characterize and learn control models for larger devices with wide classes of physically realistic Hamiltonians. This leads to a new application for small quantum computers: characterizing and controlling larger quantum computers. Our protocol achieves this by using Bayesian inference in concert with Lieb-Robinson bounds and interactive quantum learning methods to achieve compressed simulations for characterization. We illustrate the efficiency of our bootstrapping protocol by showing numerically that an 8-qubit Ising model simulator can be used to calibrate and control a 50 qubit Ising simulator while using only about 750 kilobits of experimental data.