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Integrating Neural Networks with a Quantum Simulator for State Reconstruction

Torlai, Giacomo and Timar, Brian and van Nieuwenburg, Evert P. L. and Levine, Harry and Omran, Ahmed and Keesling, Alexander and Bernien, Hannes and Greiner, Markus and Vuletić, Vladan and Lukin, Mikhail D. and Melko, Roger G. and Endres, Manuel (2019) Integrating Neural Networks with a Quantum Simulator for State Reconstruction. Physical Review Letters, 123 (23). Art. No. 230504. ISSN 0031-9007. doi:10.1103/PhysRevLett.123.230504.

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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.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Torlai, Giacomo0000-0001-8478-4436
van Nieuwenburg, Evert P. L.0000-0003-0323-0031
Omran, Ahmed0000-0002-2253-0278
Keesling, Alexander0000-0003-3931-0949
Greiner, Markus0000-0002-2935-2363
Lukin, Mikhail D.0000-0002-8658-1007
Melko, Roger G.0000-0002-5505-8176
Endres, Manuel0000-0002-4461-224X
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.
Group:Institute for Quantum Information and Matter
Funding AgencyGrant Number
Institute for Quantum Information and Matter (IQIM)UNSPECIFIED
Air Force Office of Scientific Research (AFOSR)FA9550-19-1-0044
Simons FoundationUNSPECIFIED
Natural Sciences and Engineering Research Council of Canada (NSERC)UNSPECIFIED
Canada Research Chairs ProgramUNSPECIFIED
Perimeter Institute for Theoretical PhysicsUNSPECIFIED
Industry CanadaUNSPECIFIED
Ontario Ministry of Research and InnovationUNSPECIFIED
Gordon and Betty Moore Foundation2919.01
Vannever Bush Faculty FellowshipUNSPECIFIED
Harvard-MIT Center for Ultracold AtomsUNSPECIFIED
Department of Energy (DOE)UNSPECIFIED
National Defense Science and Engineering Graduate (NDSEG) FellowshipUNSPECIFIED
Issue or Number:23
Record Number:CaltechAUTHORS:20190809-100641765
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97718
Deposited By: Tony Diaz
Deposited On:09 Aug 2019 17:46
Last Modified:16 Nov 2021 17:33

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