Quantum Bootstrapping

Christopher Granade1, 2, joint work with Nathan Wiebe3, Christopher Ferrie4, and D. G. Cory1,5,6,7

Primarily based on arXiv:1409.1524, with a review of arXiv:1207.1655 and arXiv:1404.5275.

To be presented in the week of 7 September, as a seminar at the University of Sydney.

Slides: PDF LaTeX

Abstract

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.

Bibliography

View on Zotero

Affiliations

  1. Institute for Quantum Computing, University of Waterloo.
  2. Department of Physics, University of Waterloo.
  3. Microsoft Research.
  4. Center for Quantum Information and Control, University of New Mexico.
  5. Department of Chemistry, University of Waterloo.
  6. Perimeter Institute for Theoretical Physics.
  7. Canadian Institute for Advanced Research.