典型文献
Determination of quantum toric error correction code threshold using convolutional neural network decoders
文献摘要：
Quantum error correction technology is an important solution to solve the noise interference generated during the operation of quantum computers.In order to find the best syndrome of the stabilizer code in quantum error correction,we need to find a fast and close to the optimal threshold decoder.In this work,we build a convolutional neural network (CNN)decoder to correct errors in the toric code based on the system research of machine learning.We analyze and optimize various conditions that affect CNN,and use the RestNet network architecture to reduce the running time.It is shortened by 30％-40％,and we finally design an optimized algorithm for CNN decoder.In this way,the threshold accuracy of the neural network decoder is made to reach 10.8％,which is closer to the optimal threshold of about 11％.The previous threshold of 8.9％-10.3％ has been slightly improved,and there is no need to verify the basic noise.
文献关键词：
中图分类号：
作者姓名：
Hao-Wen Wang;Yun-Jia Xue;Yu-Lin Ma;Nan Hua;Hong-Yang Ma
作者机构：
School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266033,China;School of Sciences,Qingdao University of Technology,Qingdao 266033,China
文献出处：
引用格式：
[1]Hao-Wen Wang;Yun-Jia Xue;Yu-Lin Ma;Nan Hua;Hong-Yang Ma-.Determination of quantum toric error correction code threshold using convolutional neural network decoders)[J].中国物理B（英文版）,2022(01):156-163
A类：
RestNet
B类：
Determination,quantum,toric,correction,threshold,using,convolutional,neural,network,decoders,Quantum,technology,important,solution,solve,noise,interference,generated,during,operation,computers,In,order,find,best,syndrome,stabilizer,we,need,fast,optimal,this,build,errors,system,research,machine,learning,We,analyze,various,conditions,that,affect,use,architecture,reduce,running,It,shortened,by,finally,design,optimized,algorithm,way,accuracy,made,reach,which,closer,about,previous,has,been,slightly,improved,there,verify,basic
AB值：
0.49975
相似文献
机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
