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Hamiltonian learning for quantum error correction

Valenti, Agnes and van Nieuwenburg, Evert and Huber, Sebastian and Greplova, Eliska (2019) Hamiltonian learning for quantum error correction. Physical Review Research, 1 (3). Art. No. 033092. ISSN 2643-1564. https://resolver.caltech.edu/CaltechAUTHORS:20191112-093455800

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Abstract

The efficient validation of quantum devices is critical for emerging technological applications. In a wide class of use cases the precise engineering of a Hamiltonian is required both for the implementation of gate-based quantum information processing as well as for reliable quantum memories. Inferring the experimentally realized Hamiltonian through a scalable number of measurements constitutes the challenging task of Hamiltonian learning. In particular, assessing the quality of the implementation of topological codes is essential for quantum error correction. Here, we introduce a neural-net-based approach to this challenge. We capitalize on a family of exactly solvable models to train our algorithm and generalize to a broad class of experimentally relevant sources of errors. We discuss how our algorithm scales with system size and analyze its resilience toward various noise sources.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1103/physrevresearch.1.033092DOIArticle
https://arxiv.org/abs/1907.02540arXivDiscussion Paper
ORCID:
AuthorORCID
van Nieuwenburg, Evert0000-0003-0323-0031
Huber, Sebastian0000-0003-3558-351X
Greplova, Eliska0000-0003-3369-2544
Additional Information:© 2019 Published by the American Physical Society. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Received 14 July 2019; published 11 November 2019. We acknowledge discussions with Aleksander Kubica, Tomas Jochym O'Connor, Gil Refael, Netanel Lindner, Christoph Bruder, and Mohammad Hafezi. We are grateful for financial support from the Swiss National Science Foundation, the NCCR QSIT. This work has received funding from the European Research Council under Grant Agreement No. 771503. This research was supported in part by the National Science Foundation under Grant No. NSF PHY-1748958 and by the Swiss National Science Foundation under Grant No. 183945. A.V. acknowledges financial support of the ETH Master Scholarship Programme.
Group:UNSPECIFIED, Institute for Quantum Information and Matter
Funders:
Funding AgencyGrant Number
Swiss National Science Foundation (SNSF)183945
European Research Council (ERC)771503
NSFPHY-1748958
ETH ZurichUNSPECIFIED
Issue or Number:3
Record Number:CaltechAUTHORS:20191112-093455800
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20191112-093455800
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:99798
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:12 Nov 2019 17:44
Last Modified:04 Jun 2020 10:14

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