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Advantages of versatile neural-network decoding for topological codes

Maskara, Nishad and Kubica, Aleksander and Jochym-O'Connor, Tomas (2019) Advantages of versatile neural-network decoding for topological codes. Physical Review A, 99 (5). Art. No. 052351. ISSN 2469-9926. http://resolver.caltech.edu/CaltechAUTHORS:20190212-153409929

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Abstract

Finding optimal correction of errors in generic stabilizer codes is a computationally hard problem, even for simple noise models. While this task can be simplified for codes with some structure, such as topological stabilizer codes, developing good and efficient decoders still remains a challenge. In our paper, we systematically study a very versatile class of decoders based on feedforward neural networks. To demonstrate adaptability, we apply neural decoders to the triangular color and toric codes under various noise models with realistic features, such as spatially correlated errors. We report that neural decoders provide a significant improvement over leading efficient decoders in terms of the error-correction threshold. In particular, the neural decoder threshold for the two-dimensional color code is very close to the toric code threshold. Using neural networks simplifies the design of decoders and does not require prior knowledge of the underlying noise.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1103/PhysRevA.99.052351DOIArticle
https://arxiv.org/abs/1802.08680arXivDiscussion Paper
Additional Information:© 2019 American Physical Society. Received 25 January 2019; published 30 May 2019. We would like to thank Ben Brown, Jenia Mozgunov, and John Preskill for valuable discussions, as well as Evert van Nieuwenburg for his feedback on this paper. N.M. acknowledges funding provided by the Caltech SURF program. A.K. acknowledges funding provided by the Simons Foundation through the “It from Qubit” Collaboration. Research at Perimeter Institute is supported by the Government of Canada through Industry Canada and by the Province of Ontario through the Ministry of Research and Innovation. T.J. acknowledges support from the Walter Burke Institute for Theoretical Physics in the form of the Sherman Fairchild Fellowship. The authors acknowledge the support from the Institute for Quantum Information and Matter, an NSF Physics Frontiers Center (NFS Grant PHY1733907).
Group:IQIM, Institute for Quantum Information and Matter, Walter Burke Institute for Theoretical Physics
Funders:
Funding AgencyGrant Number
Caltech Summer Undergraduate Research Fellowship (SURF)UNSPECIFIED
Simons FoundationUNSPECIFIED
Industry CanadaUNSPECIFIED
Ontario Ministry of Research and InnovationUNSPECIFIED
Walter Burke Institute for Theoretical Physics, CaltechUNSPECIFIED
Sherman Fairchild FoundationUNSPECIFIED
Institute for Quantum Information and Matter (IQIM)UNSPECIFIED
NSFPHY-1733907
Record Number:CaltechAUTHORS:20190212-153409929
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190212-153409929
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:92854
Collection:CaltechAUTHORS
Deposited By: Bonnie Leung
Deposited On:15 Feb 2019 21:32
Last Modified:31 May 2019 16:40

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