Reinforcement learning for optimal error correction of toric codes.

• Reinforcement learning can efficiently decode uncorrelated quantum errors in toric codes. • Code threshold is very close to the 11% theoretical bound. • Policy free deep Q-learning is sufficiently versatile to address correlated noise. • Free deep Q-learning for uncorrelated noise yields essentially minimum weight matching pattern. 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. [ABSTRACT FROM AUTHOR]