Quantum error correction for heavy hexagonal code using deep reinforcement learning with policy reuse

Abstract

Quantum error correction techniques are important for implementing fault-tolerant quantum computation, and topological quantum error correcting codes provide feasibility for implementing large-scale fault-tolerant quantum computation. Here, we propose a deep reinforcement learning framework for implementing quantum error correction algorithms for errors on heavy hexagonal codes. Specifically, we construct the double deep Q learning model with policy reuse method, so that the decoding agent does not have to explore the learning from scratch when dealing with new error syndrome, but instead reuses past policies, which can reduce the computational complexity. And the double deep Q network can avoid the problem of threshold being overestimated and get the true decoding threshold. Our experimental results show that the error correction accuracy of our decoder can reach 91.86%. Different thresholds are obtained according to the code distance, which is 0.0058 when the code distance is 3, 5, 7, and 0.0065 when the code distance is 5, 7, 9, both higher than that of the classical minimum weight perfect matching decoder. Compared to the threshold of the MWPM decoder under the depolarizing noise model, the threshold of our decoder is improved by 32.63%, which enables better fault-tolerant quantum computation.