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Decoding topological XYZ2 codes with reinforcement learning based on attention mechanisms |
| Qing-Hui Chen(陈庆辉)1, Yu-Xin Ji(姬宇欣)1, Ke-Han Wang(王柯涵)2, Hong-Yang Ma(马鸿洋)2, and Nai-Hua Ji(纪乃华)1,† |
1 School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China;
2 School of Sciences, Qingdao University of Technology, Qingdao 266033, China |
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Abstract Quantum error correction, a technique that relies on the principle of redundancy to encode logical information into additional qubits to better protect the system from noise, is necessary to design a viable quantum computer. For this new topological stabilizer code-$XYZ^{2}$ code defined on the cellular lattice, it is implemented on a hexagonal lattice of qubits and it encodes the logical qubits with the help of stabilizer measurements of weight six and weight two. However topological stabilizer codes in cellular lattice quantum systems suffer from the detrimental effects of noise due to interaction with the environment. Several decoding approaches have been proposed to address this problem. Here, we propose the use of a state-attention based reinforcement learning decoder to decode $XYZ^{2}$ codes, which enables the decoder to more accurately focus on the information related to the current decoding position, and the error correction accuracy of our reinforcement learning decoder model under the optimisation conditions can reach 83.27\% under the depolarizing noise model, and we have measured thresholds of 0.18856 and 0.19043 for $XYZ^{2}$ codes at code spacing of 3-7 and 7-11, respectively. our study provides directions and ideas for applications of decoding schemes combining reinforcement learning attention mechanisms to other topological quantum error-correcting codes.
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Received: 14 December 2023
Revised: 18 February 2024
Accepted manuscript online: 15 March 2024
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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: This work was supported by the Natural Science Foundation of Shandong Province, China (Grant No. ZR2021MF049) and Joint Fund of Natural Science Foundation of Shandong Province (Grant Nos. ZR2022LLZ012 and ZR2021LLZ001). |
Corresponding Authors:
Qing-Quan Jiang, Guo-Ping Li
E-mail: 13964863452@126.com
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Cite this article:
Qing-Hui Chen(陈庆辉), Yu-Xin Ji(姬宇欣), Ke-Han Wang(王柯涵), Hong-Yang Ma(马鸿洋), and Nai-Hua Ji(纪乃华) Decoding topological XYZ2 codes with reinforcement learning based on attention mechanisms 2024 Chin. Phys. B 33 060314
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