Quantum Information Protection Scheme Based on Reinforcement Learning for Periodic Surface Codes. (18th July 2022)

• Record Type: Journal Article
• Authors: Xue, Yun-Jia; Wang, Hao-Wen; Tian, Yan-Bing; Wang, Yi-Nuo; Wang, Yu-Xuan; Wang, Shu-Mei
• Other Names: Yin Liuguo Academic Editor.
• Is Part Of: Quantum engineering. Volume 2022(2022)
• Publication Date: 2022-07-18
• Subjects: Quantum theory -- Periodicals; Engineering -- Periodicals; Quantum theory; Engineering; Electronic journals; Periodicals; 530.12
• DOI: 10.1155/2022/7643871
• Languages: English
• ISSNs: 2577-0470

Abstract

Abstract : Quantum information transfer is an information processing technology with high speed and high entanglement with the help of quantum mechanics principles. To solve the problem of quantum information getting easily lost during transmission, we choose topological quantum error correction codes as the best candidate codes to improve the fidelity of quantum information. The stability of topological error correction codes brings great convenience to error correction. The quantum error correction codes represented by surface codes have produced very good effects in the error correction mechanism. In order to solve the problem of strong spatial correlation and optimal decoding of surface codes, we introduced a reinforcement learning decoder that can effectively characterize the spatial correlation of error correction codes. At the same time, we use a double-layer convolutional neural network model in the confrontation network to find a better error correction chain, and the generation network can approach the best correction model, to ensure that the discriminant network corrects more nontrivial errors. To improve the efficiency of error correction, we introduced a double-Q algorithm and ResNet network to increase the error correction success rate and training speed of the surface code. Compared with the previous MWPM 0.005 decoder threshold, the success rate has slightly improved, which can reach up to 0.0068 decoder threshold. By using the residual neural network architecture, we saved one-third of the training time and increased the training accuracy to about 96.6%. Using a better training model, we have successfully increased the decoder threshold from 0.0068 to 0.0085, and the depolarized noise model being used does not require a priori basic noise, so that the error correction efficiency of the entire model has slightly improved. Finally, the fidelity of the quantum information has successfully improved from 0.2423 to 0.7423 by using the error correction protection schemes.