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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 |
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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.
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Received: 08 February 2023
Revised: 16 May 2023
Accepted manuscript online: 25 May 2023
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PACS:
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03.67.-a
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(Quantum information)
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87.64.Aa
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(Computer simulation)
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03.67.Pp
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(Quantum error correction and other methods for protection against decoherence)
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| Fund: Project supported by the Natural Science Foundation of Shandong Province, China (Grant Nos. ZR2021MF049, ZR2022LLZ012, and ZR2021LLZ001). |
Corresponding Authors:
Hong-Yang Ma
E-mail: hongyang_ma@aliyun.com
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Cite this article:
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 2023 Chin. Phys. B 32 100307
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