Reinforcement learning for optimal error correction of toric codes

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

*Corresponding author for this work

Research output: Contribution to journalArticleResearch

6 Citations (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.

Original languageEnglish
JournalPhysics Letters, Section A: General, Atomic and Solid State Physics
Issue number17
Publication statusPublished - 15 Jun 2020


  • Error correction
  • Neural networks
  • Reinforcement learning
  • Toric code


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