CaltechAUTHORS
Published December 6, 2019 | Version Submitted + Published
Journal Article Open

Integrating Neural Networks with a Quantum Simulator for State Reconstruction

Creators

  • 1. ROR icon University of Waterloo
  • 2. ROR icon Perimeter Institute
  • 3. ROR icon California Institute of Technology
  • 4. ROR icon Harvard University
  • 5. ROR icon University of Chicago
  • 6. ROR icon Massachusetts Institute of Technology

Abstract

We demonstrate quantum many-body state reconstruction from experimental data generated by a programmable quantum simulator by means of a neural-network model incorporating known experimental errors. Specifically, we extract restricted Boltzmann machine wave functions from data produced by a Rydberg quantum simulator with eight and nine atoms in a single measurement basis and apply a novel regularization technique to mitigate the effects of measurement errors in the training data. Reconstructions of modest complexity are able to capture one- and two-body observables not accessible to experimentalists, as well as more sophisticated observables such as the Rényi mutual information. Our results open the door to integration of machine learning architectures with intermediate-scale quantum hardware.

Additional Information

© 2019 American Physical Society. Received 29 April 2019; revised manuscript received 15 September 2019; published 6 December 2019. We thank Dmitry Abanin for helpful discussions and Soonwon Choi and Hannes Pichler for pointing out the bound on Rényi entropies. M. E. and B. T. acknowledge funding provided by the Institute for Quantum Information and Matter, a NSF Physics Frontiers Center (NSF Grant No. PHY-1733907), as well as the NSF CAREER Grant No. 1753386, and the AFOSR YIP (Grant No. FA9550-19-1-0044). The Flatiron Institute is supported by the Simons Foundation. R. G. M. was supported by NSERC of Canada, a Canada Research Chair, and the Perimeter Institute for Theoretical Physics. Research at Perimeter Institute is supported through Industry Canada and by the Province of Ontario through the Ministry of Research and Innovation. This research was supported in part by NSF Grant No. PHY-1748958, NIH Grant No. R25GM067110, and the Gordon and Betty Moore Foundation Grant No. 2919.01. H. L. acknowledges support from the National Defense Science and Engineering Graduate (NDSEG) fellowship. Work at Harvard was supported by CUA, NSF, DOE and V. Bush Faculty Fellowship. G. T. and B. T. contributed equally to this work.

Attached Files

Published - PhysRevLett.123.230504.pdf

Submitted - 1904.08441.pdf

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1904.08441.pdf

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Additional details

Identifiers

Eprint ID
97718
Resolver ID
CaltechAUTHORS:20190809-100641765

Related works

Describes
https://arxiv.org/abs/1904.08441 (URL)

Funding

Institute for Quantum Information and Matter (IQIM)
NSF
PHY-1733907
NSF
PHY-1753386
Air Force Office of Scientific Research (AFOSR)
FA9550-19-1-0044
Simons Foundation
Natural Sciences and Engineering Research Council of Canada (NSERC)
Canada Research Chairs Program
Perimeter Institute for Theoretical Physics
Industry Canada
Ontario Ministry of Research and Innovation
NSF
PHY-1748958
NIH
R25GM067110
Gordon and Betty Moore Foundation
2919.01
Vannever Bush Faculty Fellowship
Harvard-MIT Center for Ultracold Atoms
Department of Energy (DOE)
National Defense Science and Engineering Graduate (NDSEG) Fellowship

Dates

Created
2019-08-09
Created from EPrint's datestamp field
Updated
2021-11-16
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Caltech Custom Metadata

Caltech groups
Institute for Quantum Information and Matter