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Intell."],"published-print":{"date-parts":[[2020,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>There has been a rise in decoding quantum error correction codes with neural network\u2013based decoders, due to the good decoding performance achieved and adaptability to any noise model. However, the main challenge is scalability to larger code distances due to an exponential increase of the error syndrome space. Note that successfully decoding the surface code under realistic noise assumptions will limit the size of the code to less than 100 qubits with current neural network\u2013based decoders. Such a problem can be tackled by a distributed way of decoding, similar to the renormalization group (RG) decoders. In this paper, we introduce a decoding algorithm that combines the concept of RG decoding and neural network\u2013based decoders. We tested the decoding performance under depolarizing noise with noiseless error syndrome measurements for the rotated surface code and compared against the blossom algorithm and a neural network\u2013based decoder. We show that a similar level of decoding performance can be achieved between all tested decoders while providing a solution to the scalability issues of neural network\u2013based decoders.<\/jats:p>","DOI":"10.1007\/s42484-020-00015-9","type":"journal-article","created":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T15:02:51Z","timestamp":1584111771000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Decoding surface code with a distributed neural network\u2013based decoder"],"prefix":"10.1007","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5277-8768","authenticated-orcid":false,"given":"Savvas","family":"Varsamopoulos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Koen","family":"Bertels","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carmen G.","family":"Almudever","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,3,13]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","first-page":"48","DOI":"10.22331\/q-2018-01-29-48","volume":"2","author":"P Baireuther","year":"2018","unstructured":"Baireuther P, O\u2019Brien TE, Tarasinski B, Beenakker CWJ (2018) Machine-learning-assisted correction of correlated qubit errors in a topological code. 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