DQN-Based Decoder for depolarizing noise on Rotated Surface Code

In quantum fault-tolerant computing, fast decoding of encoded bits is related to the running speed of quantum computers. In this paper, we propose a decoding agent based on reinforcement learning for correcting rotated surface codes in quantum error correction. The intelligent agent is trained using Deep Q-Network (DQN) and uses Convolutional Neural Network (CNN) to extract features of error syndrome and calculate the corresponding Q values for X, Y, and Z error correction operations. In the environment of depolarizing noise, the trained model achieves a fault-tolerant threshold that is superior to the Minimum Weight Perfect Matching (MWPM) and CNN decoder, providing a feasible framework for surface code error correction in practical applications.