{"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/neural-network-decoder-for-topological-color","title":"Neural network decoder for topological color codes with circuit level noise","arxiv_id":"1804.02926","date":"2018-04-09","proceeding":null,"authors":["P. Baireuther","M. D. Caio","B. Criger","C. W. J. Beenakker","T. E. O'Brien"],"abstract":"A quantum computer needs the assistance of a classical algorithm to detect and identify errors that affect encoded quantum information. At this interface of classical and quantum computing the technique of machine learning has appeared as a way to tailor such an algorithm to the specific error processes of an experiment --- without the need for a priori knowledge of the error model. Here, we apply this technique to topological color codes. We demonstrate that a recurrent neural network with long short-term memory cells can be trained to reduce the error rate $\\epsilon_{\\rm L}$ of the encoded logical qubit to values much below the error rate $\\epsilon_{\\rm phys}$ of the physical qubits --- fitting the expected power law scaling $\\epsilon_{\\rm L} \\propto \\epsilon_{\\rm phys}^{(d+1)/2}$, with $d$ the code distance. The neural network incorporates the information from \"flag qubits\" to avoid reduction in the effective code distance caused by the circuit. As a test, we apply the neural network decoder to a density-matrix based simulation of a superconducting quantum computer, demonstrating that the logical qubit has a longer life-time than the constituting physical qubits with near-term experimental parameters.","url_abs":"http://arxiv.org/abs/1804.02926v2","url_pdf":"http://arxiv.org/pdf/1804.02926v2.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"neural-network-decoder-for-topological-color","repo_url":"https://github.com/baireuther/neural_network_decoder","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":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}