Reinforcement learning for optimal error correction of toric codes

Laia Domingo Colomer, Michalis Skotiniotis, Ramon Muñoz-Tapia*

*Autor corresponent d’aquest treball

Producció científica: Contribució a una revistaArticleRecerca

14 Cites (Scopus)


We apply deep reinforcement learning techniques to design high threshold decoders for the toric code under uncorrelated noise. By rewarding the agent only if the decoding procedure preserves the logical states of the toric code, and using deep convolutional networks for the training phase of the agent, we observe near-optimal performance for uncorrelated noise around the theoretically optimal threshold of 11%. We observe that, by and large, the agent implements a policy similar to that of minimum weight perfect matchings even though no bias towards any policy is given a priori.

Idioma originalEnglish
RevistaPhysics Letters, Section A: General, Atomic and Solid State Physics
Estat de la publicacióPublicada - 15 de juny 2020


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