Quantum Bootstrapping via Compressed Quantum Hamiltonian Learning

Nathan Wiebe1, Chris Granade2, 3 and D. G. Cory2,4,5

arXiv:1409.1524

Presented as a talk at LFQIS 2014, and at the University of Sydney. Presented as a poster at QIP 2015.

Abstract

Recent work has shown that quantum simulation is a valuable tool for learning empirical models for quantum systems. We build upon these results by showing that a small quantum simulators can be used to characterize and learn control models for larger devices for 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. Whereas Fisher information analysis shows that current methods which employ short-time evolution are suboptimal, interactive quantum learning allows us to overcome this limitation. 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.

Software Resources

QInfer, a Python-language implementation of the classical portions of the algorithm presented in this work, is available from GitHub.

Bibliography

View on Zotero

Affiliations

  1. Microsoft Research.
  2. Institute for Quantum Computing, University of Waterloo.
  3. Department of Physics, University of Waterloo.
  4. Department of Chemistry, University of Waterloo.
  5. Perimeter Institute for Theoretical Physics.