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

doi:10.1088/1674-1056/acd8a9

中国物理B ›› 2023, Vol. 32 ›› Issue (10): 100307-100307.doi: 10.1088/1674-1056/acd8a9

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

Ying-Jie Qu(曲英杰)¹, Zhao Chen(陈钊)², Wei-Jie Wang(王伟杰)¹, and Hong-Yang Ma(马鸿洋)^1,†

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

收稿日期:2023-02-08 修回日期:2023-05-16 接受日期:2023-05-25 出版日期:2023-09-21 发布日期:2023-10-09
通讯作者: Hong-Yang Ma E-mail:hongyang_ma@aliyun.com
基金资助:
Project supported by the Natural Science Foundation of Shandong Province, China (Grant Nos. ZR2021MF049, ZR2022LLZ012, and ZR2021LLZ001).

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

Ying-Jie Qu(曲英杰)¹, Zhao Chen(陈钊)², Wei-Jie Wang(王伟杰)¹, and Hong-Yang Ma(马鸿洋)^1,†

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

Received:2023-02-08 Revised:2023-05-16 Accepted:2023-05-25 Online:2023-09-21 Published:2023-10-09
Contact: Hong-Yang Ma E-mail:hongyang_ma@aliyun.com
Supported by:
Project supported by the Natural Science Foundation of Shandong Province, China (Grant Nos. ZR2021MF049, ZR2022LLZ012, and ZR2021LLZ001).

摘要/Abstract

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

Abstract: 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.

Key words: fault-tolerant quantum computing, surface code, approximate error correction, reinforcement learning

中图分类号: (Quantum information)

03.67.-a

87.64.Aa (Computer simulation) 03.67.Pp (Quantum error correction and other methods for protection against decoherence)

Ying-Jie Qu(曲英杰), Zhao Chen(陈钊), Wei-Jie Wang(王伟杰), and Hong-Yang Ma(马鸿洋). Approximate error correction scheme for three-dimensional surface codes based reinforcement learning[J]. 中国物理B, 2023, 32(10): 100307-100307.

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

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

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