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在未来量子通信网络的大规模应用中, 如何根据当前用户实际情况实现资源优化配置, 比如选择最优量子密钥分发协议(quantum key distribution, QKD)和最优系统参数等, 是实现网络应用的一个重要考察指标. 传统的QKD最优协议选择以及参数优化配置方法, 大多是通过局部搜索算法来实现. 该方法需要花费大量的计算资源和时间. 为此, 本文提出了将机器学习算法应用到QKD资源优化配置之中, 通过回归机器学习的方式来同时进行不同情境下的最优协议选择以及最优协议的参数优化配置. 此外, 将包括随机森林(random forest, RF)、最近邻(k-nearest neighbor, KNN)、逻辑回归(logistic regression)等在内的多种回归机器学习模型进行对比分析. 数据仿真结果显示, 基于机器学习的新方案与基于局部搜索算法的传统方案相比, 在资源损耗方面实现了质的跨越, 而且RF在多个回归评估指标上都取得了最佳的效果. 此外, 通过残差分析, 发现以RF回归模型为代表的机器学习方案在最优协议选择以及参数优化配置方面具有很好的环境鲁棒性. 因此, 本工作将对未来量子通信网络实际应用起到重要的推进作用.In the application of quantum communication networks, it is an important task to realize the optimal allocation of resources according to the current situation. For example, We need to select the optimal quantum key distribution (QKD) protocol and parameters. Traditionally, the most commonly implemented method is the local search algorithm (LSA), which costs a lot of resources. Here in this work, we propose a machine learning based scheme, in which the regression machine learning is used to simultaneously select the optimal protocol and corresponding parameters. In addition, we make comparisons among a few machine learning models including random forest (RF), K-nearest neighbor (KNN) and logistic regression. Simulation results show that the new scheme takes much less time than the LSA scheme, and the RF achieves the best performance. In addition, through the RF residual analysis, we find that the machine learning scheme has good robustness. In conclusion, this work may play an important role in promoting the practical application of quantum communication networks.
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Keywords:
- quantum communication network /
- quantum key distribution /
- regression machine learning /
- optimal allocation of resources.
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图 2 系统参数对随机森林回归模型的重要性.
Fig. 2. Importance of system parameters to RF regression model.
表 1 系统参数的特征范围
Table 1. Characteristic range of system parameters.
Y0 ed $ \eta $ N L/km 10–10—10–5 0.00—0.06 0.1—0.9 106—1016 1—600 
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表 2 不同回归模型的评估对比
Table 2. Evaluation and comparison of different regression models.
RF KNN LR MAE 0.002 0.012 0.038 MSE 0.016 0.049 0.131 $ {\mathit{R}}^{2} $ 0.978 0.795 0.397 Accuracy 0.977 0.787 0.365
下载: 导出CSV
表 3 时间资源损耗记录表
Table 3. Time resource wastage table.
机器学习方案 传统方案 Model RF KNN LR LSA Time 1.23 s 2.95 s 5.43 s 24 h以上
下载: 导出CSV
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[1] Bennett C H, Brassard G 1984 Proceedings of IEEE International Conference on Computers, System and Signal Processing (Bangalore: IEEE) p175
[2] Busch P, Heinonen T, Lathi P 2007 Phys. Rep. 452 155
Google Scholar
[3] Wootters W K, Zurek W H 1982 Nature. 299 299
Google Scholar
[4] Hwang W Y 2003 Phys. Rev. Lett. 91 057901
Google Scholar
[5] Wang X B 2005 Phys. Rev. Lett. 94 230503
Google Scholar
[6] Lo H K, Ma X F, Chen K 2005 Phys. Rev. Lett. 94 230504
Google Scholar
[7] Makarov V, Hjelme D R 2005 J. Mod. Optic. 52 691
Google Scholar
[8] Qi B, Fung C H F, Lo H K, Ma X F 2007 Quantum. Inf. Comput. 7 73
[9] Lamas L A, Qin L, Gerhardt I, Makarov V, Kurtsiefer C 2009 New. J. Phys. 11 065003
Google Scholar
[10] Lydersen L, Wiechers C, Wittmann C, Elser D, Skaar J 2010 Nat. Photonics. 4 686
Google Scholar
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Google Scholar
[12] Braunstein S L, Pirandola S 2012 Phys. Rev. Lett. 108 130502
Google Scholar
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Google Scholar
[14] Rubenok A, Slater J A, Chan P, Lucio M I, Tittel W 2013 Phys. Rev. Lett. 111 130501
Google Scholar
[15] Tang Z Y, Liao Z F, Xu F H, Qi B, Qian L, Lo H K 2014 Phys. Rev. Lett. 112 190503
Google Scholar
[16] Liu Y, Chen T Y, Wang L J, Liang H, Shentu G L, Wang J, Cui K, Yin H L, Liu N L, Li L, Ma X F, Pelc J S, Fejer M M, Peng C Z, Zhang Q, Pan J W 2013 Phys. Rev. Lett. 111 130502
Google Scholar
[17] Zhou X Y, Ding H J, Zhang C H, Wang Q 2020 Opt. Lett. 45 4176
Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
[24] Wang S, Yin Z Q, Chen W, He D Y, Song X T, Li H W, Zhang L J, Zhou Z, Guo G C, Han Z F 2022 Nat. Photonics. 16 154
Google Scholar
[25] Ren Z A, Chen Y P, Liu J Y, Ding H J, Wang Q 2021 IEEE Commun. Lett. 25 3
Google Scholar
[26] Fan-Yuan G J, Lu F Y, Wang S, Yin Z Q, He D Y, Zhou Z, Teng J, Chen W, Guo G C, Han Z F 2021 Photon. Res. 9 1881
Google Scholar
[27] Ding H J, Liu J Y, Zhang C M, Wang Q 2020 Quantum. Inf. Comput. 19 2548
Google Scholar
[28] Xu F, Xu H, Lo H K. 2014 Phys. Rev. A. 89 052333
Google Scholar
[29] Liu J Y, Ding H J, Zhang C M, Xie S P, Wang Q 2019 Phys. Rev. Appl. 12 014059
Google Scholar
[30] Yang M, Ren C L, Ma Y C, Xiao Y, Ye X J, Song L L, Xun J S, Yung M H, Li C F, Guo G C 2019 Phys. Rev. Lett. 123 190401
Google Scholar
[31] Zhou Y H, Yu Z W, Wang X B. 2016 Phys. Rev. A. 93 042324
Google Scholar
[32] Zhang C H, Zhang C M, Wang Q. 2019 Opt. Lett. 44 1468
Google Scholar
[33] Breiman L 2001 J. Clin. Microbiol. 45 5
Google Scholar
[34] Cover T M, Hart P E 1967 IEEE Trans. Inf. Theory 13 21
Google Scholar
[35] Cox D R 1958 J. R. Stat. Soc. B 20 215
Google Scholar
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