Decoding topological XYZ^(2) codes with reinforcement learning based on attention mechanisms  

作　　者：陈庆辉 姬宇欣 王柯涵 马鸿洋 纪乃华 Qing-Hui Chen;Yu-Xin Ji;Ke-Han Wang;Hong-Yang Ma;Nai-Hua Ji(School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266033,China;School of Sciences,Qingdao University of Technology,Qingdao 266033,China)

机构地区：[1]School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266033,China [2]School of Sciences,Qingdao University of Technology,Qingdao 266033,China

出　　处：《Chinese Physics B》2024年第6期262-270,共9页中国物理B（英文版）

基　　金：supported by the Natural Science Foundation of Shandong Province,China (Grant No. ZR2021MF049);Joint Fund of Natural Science Foundation of Shandong Province (Grant Nos. ZR2022LLZ012 and ZR2021LLZ001)。

摘　　要：Quantum error correction, a technique that relies on the principle of redundancy to encode logical information into additional qubits to better protect the system from noise, is necessary to design a viable quantum computer. For this new topological stabilizer code-XYZ^(2) code defined on the cellular lattice, it is implemented on a hexagonal lattice of qubits and it encodes the logical qubits with the help of stabilizer measurements of weight six and weight two. However topological stabilizer codes in cellular lattice quantum systems suffer from the detrimental effects of noise due to interaction with the environment. Several decoding approaches have been proposed to address this problem. Here, we propose the use of a state-attention based reinforcement learning decoder to decode XYZ^(2) codes, which enables the decoder to more accurately focus on the information related to the current decoding position, and the error correction accuracy of our reinforcement learning decoder model under the optimisation conditions can reach 83.27% under the depolarizing noise model, and we have measured thresholds of 0.18856 and 0.19043 for XYZ^(2) codes at code spacing of 3–7 and 7–11, respectively. our study provides directions and ideas for applications of decoding schemes combining reinforcement learning attention mechanisms to other topological quantum error-correcting codes.

关 键 词：quantum error correction topological quantum stabilizer code reinforcement learning attention mechanism 

分 类 号：O413[理学—理论物理]