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Linear-Time Maximum Likelihood Decoding of Surface Codes over the Quantum Erasure Channel

Delfosse, Nicolas and Zémor, Gilles (2020) Linear-Time Maximum Likelihood Decoding of Surface Codes over the Quantum Erasure Channel. Physical Review Research, 2 (3). Art. No. 033042. ISSN 2643-1564. doi:10.1103/PhysRevResearch.2.033042.

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Surface codes are among the best candidates to ensure the fault tolerance of a quantum computer. In order to avoid the accumulation of errors during a computation, it is crucial to have at our disposal a fast decoding algorithm to quickly identify and correct errors as soon as they occur. We propose a linear-time maximum likelihood decoder for surface codes over the quantum erasure channel. This decoding algorithm for dealing with qubit loss is optimal both in terms of performance and speed.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Delfosse, Nicolas0000-0002-3949-981X
Zémor, Gilles0000-0002-6041-9554
Additional Information:© 2020 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 6 November 2019; accepted 6 April 2020; published 9 July 2020. N.D. thanks Jonas Anderson for his comments on a preliminary version of this work. N.D. acknowledges funding provided by the Institute for Quantum Information and Matter, an NSF Physics Frontiers Center (NSF Grant No. PHY-1125565) with the support of the Gordon and Betty Moore Foundation (GBMF-2644).
Group:Institute for Quantum Information and Matter
Funding AgencyGrant Number
Institute for Quantum Information and Matter (IQIM)UNSPECIFIED
Gordon and Betty Moore FoundationGBMF-2644
Issue or Number:3
Record Number:CaltechAUTHORS:20171108-153301401
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:83089
Deposited By: Bonnie Leung
Deposited On:14 Nov 2017 04:04
Last Modified:15 Nov 2021 19:55

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