Recurrent neural network decoding of rotated surface codes based on distributed strategy  

作　　者：李帆 李熬庆 甘启迪 马鸿洋 Fan Li;Ao-Qing Li;Qi-Di Gan;Hong-Yang Ma(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4期322-330,共9页中国物理B（英文版）

基　　金：Project supported by Natural Science Foundation of Shandong Province,China (Grant Nos.ZR2021MF049,ZR2022LLZ012,and ZR2021LLZ001)。

摘　　要：Quantum error correction is a crucial technology for realizing quantum computers.These computers achieve faulttolerant quantum computing by detecting and correcting errors using decoding algorithms.Quantum error correction using neural network-based machine learning methods is a promising approach that is adapted to physical systems without the need to build noise models.In this paper,we use a distributed decoding strategy,which effectively alleviates the problem of exponential growth of the training set required for neural networks as the code distance of quantum error-correcting codes increases.Our decoding algorithm is based on renormalization group decoding and recurrent neural network decoder.The recurrent neural network is trained through the ResNet architecture to improve its decoding accuracy.Then we test the decoding performance of our distributed strategy decoder,recurrent neural network decoder,and the classic minimum weight perfect matching(MWPM)decoder for rotated surface codes with different code distances under the circuit noise model,the thresholds of these three decoders are about 0.0052,0.0051,and 0.0049,respectively.Our results demonstrate that the distributed strategy decoder outperforms the other two decoders,achieving approximately a 5%improvement in decoding efficiency compared to the MWPM decoder and approximately a 2%improvement compared to the recurrent neural network decoder.

关 键 词：quantum error correction rotated surface code recurrent neural network distributed strategy 

分 类 号：O413[理学—理论物理] TP38[理学—物理] TP183[自动化与计算机技术—计算机系统结构]