# Quantum Bootstrapping via Compressed Quantum Hamiltonian Learning

**Nathan Wiebe**^{1}, Chris Granade^{2, 3} and D. G. Cory^{2,4,5}

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

- Microsoft Research.
- Institute for Quantum Computing, University of Waterloo.
- Department of Physics, University of Waterloo.
- Department of Chemistry, University of Waterloo.
- Perimeter Institute for Theoretical Physics.