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Deep Neural Network Probabilistic Decoder for Stabilizer Codes
Neural networks can efficiently encode the probability distribution of errors in an error correcting code. Moreover, these distributions can be conditioned on the syndromes of the corresponding errors. This paves a path forward for a decoder that employs a neural network to calculate the conditional...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5591216/ https://www.ncbi.nlm.nih.gov/pubmed/28887480 http://dx.doi.org/10.1038/s41598-017-11266-1 |
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author | Krastanov, Stefan Jiang, Liang |
author_facet | Krastanov, Stefan Jiang, Liang |
author_sort | Krastanov, Stefan |
collection | PubMed |
description | Neural networks can efficiently encode the probability distribution of errors in an error correcting code. Moreover, these distributions can be conditioned on the syndromes of the corresponding errors. This paves a path forward for a decoder that employs a neural network to calculate the conditional distribution, then sample from the distribution - the sample will be the predicted error for the given syndrome. We present an implementation of such an algorithm that can be applied to any stabilizer code. Testing it on the toric code, it has higher threshold than a number of known decoders thanks to naturally finding the most probable error and accounting for correlations between errors. |
format | Online Article Text |
id | pubmed-5591216 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-55912162017-09-13 Deep Neural Network Probabilistic Decoder for Stabilizer Codes Krastanov, Stefan Jiang, Liang Sci Rep Article Neural networks can efficiently encode the probability distribution of errors in an error correcting code. Moreover, these distributions can be conditioned on the syndromes of the corresponding errors. This paves a path forward for a decoder that employs a neural network to calculate the conditional distribution, then sample from the distribution - the sample will be the predicted error for the given syndrome. We present an implementation of such an algorithm that can be applied to any stabilizer code. Testing it on the toric code, it has higher threshold than a number of known decoders thanks to naturally finding the most probable error and accounting for correlations between errors. Nature Publishing Group UK 2017-09-08 /pmc/articles/PMC5591216/ /pubmed/28887480 http://dx.doi.org/10.1038/s41598-017-11266-1 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Krastanov, Stefan Jiang, Liang Deep Neural Network Probabilistic Decoder for Stabilizer Codes |
title | Deep Neural Network Probabilistic Decoder for Stabilizer Codes |
title_full | Deep Neural Network Probabilistic Decoder for Stabilizer Codes |
title_fullStr | Deep Neural Network Probabilistic Decoder for Stabilizer Codes |
title_full_unstemmed | Deep Neural Network Probabilistic Decoder for Stabilizer Codes |
title_short | Deep Neural Network Probabilistic Decoder for Stabilizer Codes |
title_sort | deep neural network probabilistic decoder for stabilizer codes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5591216/ https://www.ncbi.nlm.nih.gov/pubmed/28887480 http://dx.doi.org/10.1038/s41598-017-11266-1 |
work_keys_str_mv | AT krastanovstefan deepneuralnetworkprobabilisticdecoderforstabilizercodes AT jiangliang deepneuralnetworkprobabilisticdecoderforstabilizercodes |