搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于Unet网络的空间信道连续变量量子密钥分发参数优化方法

郑甜 陈宇杰 程锦 陈兰剑 刘奥 东晨

引用本文:
Citation:

基于Unet网络的空间信道连续变量量子密钥分发参数优化方法

郑甜, 陈宇杰, 程锦, 陈兰剑, 刘奥, 东晨

Parameter optimization method for space channel continuous-variable quantum key distribution based on Unet network

ZHENG Tian, CHEN Yujie, CHENG Jin, CHEN Lanjian, LIU Ao, DONG Chen
Article Text (iFLYTEK Translation)
PDF
HTML
导出引用
  • 空间信道连续变量量子密钥分发协议(continuous-variable quantum key distribution, CV-QKD)工作在光学衍射极限、通信距离极限、光电探测极限条件下, 协议参数(如调制方差$ {V}_{\mathrm{A}} $)的优化选择会影响协议的可行性. 而低轨卫星和地面站始终处于高速相对运动中, 可视窗口时间有限且在轨计算资源受限, 传统优化算法难以满足空间信道快速动态变化的实时优化需求. 本文提出了空间信道高斯调制CV-QKD的Unet网络参数预测优化方法, 搭建空间CV-QKD链路仿真平台, 改变轨道高度、天顶角等组合参数生成126575组训练数据集, 利用Unet网络的对称结构和特征融合能力实现近实时地预测调制方差$ {V}_{\mathrm{A}} $. 仿真结果表明Unet网络在6328组跨轨道高度(510—710 km)和过量噪声水平(0.01—0.03)的测试数据中可以达到99.25%—99.41%的预测准确率, 同时相较于局部搜索算法14754 s的基准耗时, Unet将推理时间缩短至1.08 s (加速比达1.48 × 106), 为后续空间信道CV-QKD实验的参数实时优化提供了理论支撑.
    Continuous-variable quantum key distribution (CV-QKD) has made significant progress in the field of quantum communication, operating under strict conditions such as optical diffraction limit, maximum communication distance, and photoelectric detection limit. The optimization of protocol parameters, particularly the modulation variance ($ {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.
  • 图 1  基于Unet网络的自由空间高斯调制CV-QKD参数优化示意图

    Fig. 1.  Schematic diagram of parameter optimization for free-space Gaussian-modulated CV-QKD based on a Unet network.

    图 2  基于Unet网络的轻量化网络结构流程

    Fig. 2.  Schematic of the lightweight network architecture based on a Unet network.

    图 3  基于Unet网络的训练和验证损失曲线

    Fig. 3.  Training and validation loss curves based on a Unet network.

    图 4  Unet模型和传统的局部搜索算法预测$ {V}_{{\mathrm{A}}} $对比

    Fig. 4.  Comparison of predictions between the Unet model and traditional local search algorithms.

    图 5  Unet网络与局部搜索算法预测结果一致性分析

    Fig. 5.  Consistency analysis of prediction results between the Unet network and local search algorithms.

    图 6  不同过量噪声和轨道高度下不同优化方式得到的$ {V}_{\mathrm{A}} $值随通信时间的变化

    Fig. 6.  Curves of $ {V}_{\mathrm{A}} $ values over communication time under different optimization methods for various excess noise levels and orbital heights.

    图 7  不同轨道高度下, 不同调制方差$ {V}_{{\mathrm{A}}} $优化方法对密钥率生成的影响

    Fig. 7.  Influences of different $ {V}_{{\mathrm{A}}} $ optimization methods on key rate generation under various orbital heights.

    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
    下载: 导出CSV

    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.

    SetMethod$ \xi /\mathrm{S}. \mathrm{N}. \mathrm{U} $H/kmSizeRate
    /%
    Times
    /s
    Test
    data
    local
    search
    0.01–0.03400–800632899.4114754.3
    Test
    data
    Unet0.01–0.03400–80063280.16
    Test
    orbit1
    local
    search
    0.01, 0.02,
    0.03
    51044699.361031.4
    Test
    orbit1
    Unet0.01, 0.02,
    0.03
    5104460.0113
    Test
    orbit2
    local
    search
    0.01, 0.02,
    0.03
    61051899.251185.5
    Test
    orbit2
    Unet0.01, 0.02,
    0.03
    6105180.0131
    Test
    orbit3
    local
    search
    0.01, 0.02,
    0.03
    71058299.281330.9
    Test
    orbit3
    Unet0.01, 0.02,
    0.03
    7105820.0147
    下载: 导出CSV
  • [1]

    罗一振, 马洛嘉, 孙铭烁 2024 物理学报 73 240301

    Luo Y Z, Ma L J, Sun M S 2024 Acta Phys. Sin. 73 240301

    [2]

    Liao S K, Cai W Q, Liu W Y, Zhang K, Li J, Ren J G, Yin J, Shen Q, Cao Y, Li Z P, Wu F C, Li W Q, Liu S L, Ren J G, Peng C Z, Chen X W, Pan J W 2017 Nature 549 43Google Scholar

    [3]

    Yin J, Li Y H, Liao S K, Yang M, Cao Y, Zhang L, Ren J G, Cai W Q, Liu W Y, Li S L, Shu R, Huang Y M, Deng L, Li L, Zhang Q, Liu N L, Chen Y A, Lu C Y, Wang X B, Peng C Z, Pan J W 2020 Nature 582 501Google Scholar

    [4]

    Ren J G, Xu P, Yong H L, Zhang L, Liao S K, Yin J, Liu W Y, Cai W Q, Yang M, Li L, Yang K X, Han X, Yao Y Q, Li J, Wu H Y, Wan S L, Liu L, Liu D Q, Kuang Y W, He Z P, Shang P, Guo C, Zheng K H, Tian K, Zhu Z C, Liu N L, Lu C Y, Shu R, Chen Y A, Peng C Z, Wang X B, Pan J W 2017 Nature 549 70Google Scholar

    [5]

    Dubey U, Bhole P, Dutta A, Goyal S K, Behera B K, Panigrahi P K 2023 arXiv 2309.13417

    [6]

    Harney C, Fletcher A I, Pirandola S 2022 Phys. Rev. Appl. 18 014012Google Scholar

    [7]

    Long N K, Malaney R, Grant K J 2023 Information 14 553Google Scholar

    [8]

    Zhou Z C, Guo Y 2024 Electronics 13 1410Google Scholar

    [9]

    Wang W, Lo H K 2019 Phys. Rev. A 100 062334Google Scholar

    [10]

    Jin D, Guo Y, Wang Y, Li Y B, Wang T Y 2021 Phys. Rev. A 104 012616Google Scholar

    [11]

    Long N K, Malaney R, Grant K J 2024 Proc. SPIE 13106 1310602

    [12]

    Liu Z P, Zhou M G, Liu W B, Wang P, Liu J Y, Guo Y 2022 Opt. Express 30 15024Google Scholar

    [13]

    Mao Y, Huang W, Zhong H, Liao Q, Zhang S L, Guo Y 2020 New J. Phys. 22 083073Google Scholar

    [14]

    Liao Q, Xiao G, Zhong H, Guo Y, Huang D 2020 New J. Phys. 22 083086Google Scholar

    [15]

    Liu W Q, Huang P, Peng J Y, Fan J P, Zeng G H 2018 Phys. Rev. A 97 022316Google Scholar

    [16]

    殷晓航, 王永才, 李德英 2021 软件学报 32 519

    Yin X H, Wang Y C, Li D Y 2021 J. Software 32 519

    [17]

    陈宇杰, 程锦, 孙新, 刘胜豪, 张一鸣 2025 中国激光 52 330

    Chen Y J, Cheng J, Sun X, Liu S H, Zhang Y M 2025 Chin. J. Lasers 52 330

    [18]

    Hornik K, Stinchcombe M, White H 1989 Neural Networks 2 359Google Scholar

    [19]

    Liao S K, Lin J, Ren J G, Liu C, Liang H, Yin J, Cao Y, Wu F C, Li S L, Li H, Shu R, Xue G, Li B, Shen Q, Jiang L, Yang L, Wang Z, You L X, Wang Z, Pan J W 2017 Chin. Phys. Lett. 34 090302Google Scholar

    [20]

    Pirandola S 2021 Phys. Rev. Research 3 023130Google Scholar

    [21]

    Weedbrook C, Lance A M, Bowen W P, Symul T, Ralph T C, Lam P K 2004 Phys. Rev. Lett. 93 170504Google Scholar

    [22]

    Katoch S, Chauhan S S, Kumar V 2021 Multimedia Tools Appl. 80 8091Google Scholar

  • [1] 张童, 王加豪, 田帅, 孙旭冉, 李日. 基于机器学习的铸件凝固过程动态收缩行为. 物理学报, doi: 10.7498/aps.74.20241581
    [2] 王普, 白增亮, 常利伟. 一维高斯调制连续变量量子密钥分发现实源强度误差的影响. 物理学报, doi: 10.7498/aps.74.20250025
    [3] 孙新, 郭俊杰, 陈宇杰, 程锦, 刘奥, 刘文博, 尹鹏, 陈兰剑, 吴田宜, 东晨. 空间信道离散调制连续变量量子密钥分发可行性分析. 物理学报, doi: 10.7498/aps.74.20241682
    [4] 张孙成, 韩同伟, 王如盟, 杨艳陶, 张小燕. 基于机器学习的菱形穿孔石墨烯负泊松比效应预测与优化. 物理学报, doi: 10.7498/aps.74.20241624
    [5] 王鹏, 麦麦提尼亚孜·麦麦提阿卜杜拉. 机器学习的量子动力学. 物理学报, doi: 10.7498/aps.74.20240999
    [6] 张旭, 丁进敏, 侯晨阳, 赵一鸣, 刘鸿维, 梁生. 基于机器学习的激光匀光整形方法. 物理学报, doi: 10.7498/aps.73.20240747
    [7] 张嘉晖. 蛋白质计算中的机器学习. 物理学报, doi: 10.7498/aps.73.20231618
    [8] 张光伟, 白建东, 颉琦, 靳晶晶, 张永梅, 刘文元. 连续变量量子密钥分发系统中动态偏振控制研究. 物理学报, doi: 10.7498/aps.73.20231890
    [9] 吴晓东, 黄端. 基于非理想测量基选择的水下连续变量量子密钥分发方案. 物理学报, doi: 10.7498/aps.73.20240804
    [10] 张云杰, 王旭阳, 张瑜, 王宁, 贾雁翔, 史玉琪, 卢振国, 邹俊, 李永民. 基于硬件同步的四态离散调制连续变量量子密钥分发. 物理学报, doi: 10.7498/aps.73.20231769
    [11] 管星悦, 黄恒焱, 彭华祺, 刘彦航, 李文飞, 王炜. 生物分子模拟中的机器学习方法. 物理学报, doi: 10.7498/aps.72.20231624
    [12] 周江平, 周媛媛, 周学军. 改进的测量设备无关协议参数优化方法. 物理学报, doi: 10.7498/aps.72.20230179
    [13] 吴晓东, 黄端. 基于非高斯态区分探测的往返式离散调制连续变量量子密钥分发方案. 物理学报, doi: 10.7498/aps.72.20222253
    [14] 廖骎, 柳海杰, 王铮, 朱凌瑾. 基于不可信纠缠源的高斯调制连续变量量子密钥分发. 物理学报, doi: 10.7498/aps.72.20221902
    [15] 刘天乐, 徐枭, 付博玮, 徐佳歆, 刘靖阳, 周星宇, 王琴. 基于回归决策树的测量设备无关型量子密钥分发参数优化. 物理学报, doi: 10.7498/aps.72.20230160
    [16] 陈江芷, 杨晨温, 任捷. 基于波动与扩散物理系统的机器学习. 物理学报, doi: 10.7498/aps.70.20210879
    [17] 刘武, 朱成皖, 李昊天, 赵谡玲, 乔泊, 徐征, 宋丹丹. 基于机器学习和器件模拟对Cu(In,Ga)Se2电池中Ga含量梯度的优化分析. 物理学报, doi: 10.7498/aps.70.20211234
    [18] 林键, 叶梦, 朱家纬, 李晓鹏. 机器学习辅助绝热量子算法设计. 物理学报, doi: 10.7498/aps.70.20210831
    [19] 沈咏, 邹宏新. 离散调制连续变量量子密钥分发的安全边界. 物理学报, doi: 10.7498/aps.59.1473
    [20] 张新陆, 王月珠, 李 立, 崔金辉, 鞠有伦. 端面抽运Tm,Ho∶YLF连续激光器的参数优化与实验研究. 物理学报, doi: 10.7498/aps.57.3519
计量
  • 文章访问数:  428
  • PDF下载量:  6
  • 被引次数: 0
出版历程
  • 收稿日期:  2025-06-08
  • 修回日期:  2025-07-04
  • 上网日期:  2025-07-24

/

返回文章
返回