AUTHOR=Wang  Hao-Wen , Cao  Qian , Xue  Yun-Jia , Ding  Li , Liu  Han-Yang , Dong  Yu-Min , Ma  Hong-Yang TITLE=Determining quantum topological semion code decoder performance and error correction effectiveness with reinforcement learning JOURNAL=Frontiers in Physics VOLUME=Volume 10 - 2022 YEAR=2022 URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2022.981225 DOI=10.3389/fphy.2022.981225 ISSN=2296-424X ABSTRACT=

Quantum error correction technology is a vital method to eliminate noise during the operation of quantum computers. To solve the problem caused by noise, in this paper, reinforcement learning is used to encode defects of Semion codes, and the experience replay technique is used to realize the design of decoder. Semion codes are quantum topological error correction codes with the same symmetry group Z2 as Kitaev toric codes, we used the topological characteristics of error correction codes to map qubits to multi-dimensional space, and error correction accuracy of the decoder is calculated to be 77.5%. Calculate the threshold of topological quantum Semion code, depending on the code distance, resulting in different thresholds, pthreshold = 0.081574 when the code distance is d = 3, 5, 7 and threshold pthreshold = 0.09542 when the code distance is d = 5, 7, 9. And we design the Q-network to optimize the cost of quantum circuit gates and compare the size of the cost reduction under different thresholds. Reinforcement learning is an important method for designing Semion code decoders and optimizing numerical values, providing more general error models and error correction codes for future machine engineering decoders.