{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-neural-decoders-for-near-term-fault","title":"Deep neural decoders for near term fault-tolerant experiments","arxiv_id":"1802.06441","date":"2018-02-18","proceeding":null,"authors":["Christopher Chamberland","Pooya Ronagh"],"abstract":"Finding efficient decoders for quantum error correcting codes adapted to\nrealistic experimental noise in fault-tolerant devices represents a significant\nchallenge. In this paper we introduce several decoding algorithms complemented\nby deep neural decoders and apply them to analyze several fault-tolerant error\ncorrection protocols such as the surface code as well as Steane and Knill error\ncorrection. Our methods require no knowledge of the underlying noise model\nafflicting the quantum device making them appealing for real-world experiments.\nOur analysis is based on a full circuit-level noise model. It considers both\ndistance-three and five codes, and is performed near the codes pseudo-threshold\nregime. Training deep neural decoders in low noise rate regimes appears to be a\nchallenging machine learning endeavour. We provide a detailed description of\nour neural network architectures and training methodology. We then discuss both\nthe advantages and limitations of deep neural decoders. Lastly, we provide a\nrigorous analysis of the decoding runtime of trained deep neural decoders and\ncompare our methods with anticipated gate times in future quantum devices.\nGiven the broad applications of our decoding schemes, we believe that the\nmethods presented in this paper could have practical applications for near term\nfault-tolerant experiments.","url_abs":"http://arxiv.org/abs/1802.06441v2","url_pdf":"http://arxiv.org/pdf/1802.06441v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-neural-decoders-for-near-term-fault","repo_url":"https://github.com/pooya-git/DeepNeuralDecoder","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.06441","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}