Phase Compensation for Continuous Variable Quantum Key Distribution Based on Convolutional Neural Network

• Main Authors: Li, X. (Author), Luo, Y. (Author), Ruan, X. (Author), Xing, Z. (Author), Zhang, H. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: convolution neural network; local local oscillation continuous variable quantum key distribution; phase compensation; phase drift
• Online Access: View Fulltext in Publisher

 Phase drift extremely limits the secure key rate and secure transmission distance, which is non-negligible in local oscillation continuous variable quantum key distribution (LLO CV-QKD). In order to eliminate the impact caused by phase drift, we analyze the phase noise of the system and propose a phase compensation method based on convolutional neural network (CNN). Moreover, the compensation is performed on the signal according to the estimated value of phase drift before coherent detection. In numerical simulation, we compare the performance of phase compensation methods based on CNN and Kalman filter (KF), and the results show that CNN-based phase compensation has higher accuracy and stability. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.