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Continuous-variable quantum key distribution (CV-QKD) has made significant progress in the field of quantum communication, operating under strict conditions such as optical diffraction limit, maximum communication distance, and photoelectric detection limit. The optimization of protocol parameters, particularly the modulation variance ($ {V}_{\mathrm{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 satellites and ground stations, coupled with limited on-board computing resources, poses challenges for traditional optimization algorithms to meet the real-time demands of rapidly changing space channels. To cope with these challenges, a novel method of optimizing Gaussian-modulation CV-QKD in space channels using a Unet-based approach is proposed in this work. A comprehensive simulation platform for CV-QKD links, generating a substantial training dataset of 126575 samples by changing parameters such as orbital height and zenith angle, is developed in this work. 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 6328 datasets, orbital heights between 510 and 710 km, and excess noise levels between 0.01 and 0.03. Compared with the local search algorithm, which takes 14754 s, the Unet-based approach significantly reduces the inference time to just 1.08 s, representing a speed-up ratio of 1.48 × 106. These findings provide a solid theoretical foundation for optimizing real-time parameters in future space-channel CV-QKD experiments, and have made significant progress 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. -
表 1 CelesTrak提供的两行轨道根数(TLE)数据
Table 1. Two-line element set (TLE) data from CelesTrak.
参数 描述 数值 历元时间 数据发布日期 16354.569 轨道倾角 轨道平面与赤道平面的夹角 97.3698° 升交点赤经 轨道与赤道交点的经度 268.1064° 偏心率 轨道椭圆程度 0.0013349 近地点幅角 升交点与近地点之间的夹角 175.8929° 平近点角 卫星在历元时刻的轨道位置 309.019° 历元时间 数据发布日期 16354.569 表 2 不同测试集在不同网络中的表现
Table 2. Performance of different test sets across various networks.
Set Method $ \xi /\mathrm{S}. \mathrm{N}. \mathrm{U} $ H/km Size Rate
/%Times
/sTest
datalocal
search0.01–0.03 400–800 6328 99.41 14754.3 Test
dataUnet 0.01–0.03 400–800 6328 0.16 Test
orbit1local
search0.01, 0.02,
0.03510 446 99.36 1031.4 Test
orbit1Unet 0.01, 0.02,
0.03510 446 0.0113 Test
orbit2local
search0.01, 0.02,
0.03610 518 99.25 1185.5 Test
orbit2Unet 0.01, 0.02,
0.03610 518 0.0131 Test
orbit3local
search0.01, 0.02,
0.03710 582 99.28 1330.9 Test
orbit3Unet 0.01, 0.02,
0.03710 582 0.0147 -
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