The field of quantum communication has seen significant advancements with Continuous-Variable Quantum Key Distribution (CV-QKD), which operates under stringent conditions such as optical diffraction limits, maximum communication distances, and photoelectric detection limits. The optimization of protocol parameters, particularly the modulation variance (V_A), is crucial for the feasibility of CV-QKD. However, in space-to-ground CV-QKD scenarios, the high-speed relative motion between low-earth-orbit (LEO) satellites and ground stations, coupled with limited on-board computing resources, poses challenges for conventional optimization algorithms to meet the real-time demands of rapidly changing space channels.
To address these challenges, this paper introduces a novel method for optimizing Gaussian-modulation CV-QKD in space channels using a Unet-based approach. We have developed a comprehensive simulation platform for CV-QKD links, generating a substantial training dataset of 126,575 samples by varying parameters such as orbital height and zenith angle. The Unet network, renowned for its symmetric architecture and powerful feature fusion capabilities, is utilized to achieve near-real-time prediction of modulation variance. Our simulation results demonstrate the effectiveness of the proposed method, with the Unet network achieving a remarkable prediction accuracy of 99.25%–99.41% on 6,328 datasets with orbital heights ranging from 510 to 710 km and excess noise levels between 0.01 and 0.03.
Compared to the local search algorithm, which takes 14,754 seconds, the Unet-based approach significantly reduces the inference time to just 1.08 seconds, representing a speed-up ratio of 1.48×10⁶. These findings provide a solid theoretical foundation for real-time parameter optimization in future space-channel CV-QKD experiments, offering a significant advancement in the field of quantum communication. The proposed method not only enhances the efficiency of parameter optimization but also ensures the security and reliability of CV-QKD in dynamic space environments.