| Determination of quantum toric error correction code threshold using convolutional neural network decoders |
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| 作者姓名： | 王浩文 薛韵佳 马玉林 华南 马鸿洋 |
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| 作者单位： | 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 |
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| 基金项目： | the National Natural Science Foundation of China(Grant Nos.11975132 and 61772295);the Natural Science Foundation of Shandong Province,China(Grant No.ZR2019YQ01);the Project of Shandong Province Higher Educational Science and Technology Program,China(Grant No.J18KZ012). |
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| 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.
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| 关 键 词： | quantum error correction toric code convolutional neural network(CNN)decoder |
| 收稿时间： | 2021-06-07 |
Determination of quantum toric error correction code threshold using convolutional neural network decoders |
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| 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].Chinese Physics B,2022,31(1):10303-010303. |
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| Authors: | Hao-Wen Wang Yun-Jia Xue Yu-Lin Ma Nan Hua Hong-Yang Ma |
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| Institution: | 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 |
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| Abstract: | 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. |
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| Keywords: | quantum error correction toric code convolutional neural network (CNN) decoder |
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