Approximate error correction scheme for three-dimensional surface codes based reinforcement learning  

作　　者：曲英杰 陈钊 王伟杰 马鸿洋 Ying-Jie Qu;Zhao Chen;Wei-Jie Wang;Hong-Yang Ma(School of Sciences,Qingdao University of Technology,Qingdao 266033,China;School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266033,China)

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

出　　处：《Chinese Physics B》2023年第10期229-240,共12页中国物理B（英文版）

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

摘　　要：Quantum error correction technology is an important method to eliminate errors during the operation of quantum computers.In order to solve the problem of influence of errors on physical qubits,we propose an approximate error correction scheme that performs dimension mapping operations on surface codes.This error correction scheme utilizes the topological properties of error correction codes to map the surface code dimension to three dimensions.Compared to previous error correction schemes,the present three-dimensional surface code exhibits good scalability due to its higher redundancy and more efficient error correction capabilities.By reducing the number of ancilla qubits required for error correction,this approach achieves savings in measurement space and reduces resource consumption costs.In order to improve the decoding efficiency and solve the problem of the correlation between the surface code stabilizer and the 3D space after dimension mapping,we employ a reinforcement learning(RL)decoder based on deep Q-learning,which enables faster identification of the optimal syndrome and achieves better thresholds through conditional optimization.Compared to the minimum weight perfect matching decoding,the threshold of the RL trained model reaches 0.78%,which is 56%higher and enables large-scale fault-tolerant quantum computation.

关 键 词：fault-tolerant quantum computing surface code approximate error correction reinforcement learning 

分 类 号：O413[理学—理论物理] TN911.22[理学—物理]