Integrating Neural Networks with a Quantum Simulator for State Reconstruction
Name
PhysRevLett.123.230504.pdf
Description
Published version
Size
463.29 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Torlai, Giacomo
Timar, Brian
van Nieuwenburg, Evert PL
Levine, Harry
Omran, Ahmed
Keesling, Alexander
Bernien, Hannes
Greiner, Markus
Vuletić, Vladan
Lukin, Mikhail D
Date Issued
2019
Journal
Physical Review Letters
Publisher
American Physical Society (APS)
Version
Final published version
Abstract
© 2019 American Physical Society. 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.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Massachusetts Institute of Technology. Research Laboratory of Electronics
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1103/PHYSREVLETT.123.230504
