{"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/reinforcement-learning-decoders-for-fault","title":"Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation","arxiv_id":"1810.07207","date":"2018-10-16","proceeding":null,"authors":["Ryan Sweke","Markus S. Kesselring","Evert P. L. van Nieuwenburg","Jens Eisert"],"abstract":"Topological error correcting codes, and particularly the surface code,\ncurrently provide the most feasible roadmap towards large-scale fault-tolerant\nquantum computation. As such, obtaining fast and flexible decoding algorithms\nfor these codes, within the experimentally relevant context of faulty syndrome\nmeasurements, is of critical importance. In this work, we show that the problem\nof decoding such codes, in the full fault-tolerant setting, can be naturally\nreformulated as a process of repeated interactions between a decoding agent and\na code environment, to which the machinery of reinforcement learning can be\napplied to obtain decoding agents. As a demonstration, by using deepQ learning,\nwe obtain fast decoding agents for the surface code, for a variety of\nnoise-models.","url_abs":"http://arxiv.org/abs/1810.07207v1","url_pdf":"http://arxiv.org/pdf/1810.07207v1.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":"reinforcement-learning-decoders-for-fault","repo_url":"https://github.com/R-Sweke/DeepQ-Decoding","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.07207","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}