Research progress of artificial intelligence empowered quantum communication and quantum sensing systems

XU Jiaxin; XU Lechen; LIU Jingyang; DING Huajian; WANG Qin

doi:10.7498/aps.74.20250322

Abstract

Quantum communication and quantum sensing, which leverage the unique characteristics of quantum systems, enable information-theoretically secure communication and high-precision measurement of physical quantities. They have attracted significant attention in recent research. However, they both face numerous challenges on the path to practical application. For instance, device imperfections may lead to security vulnerability, and environmental noise may significantly reduce measurement accuracy. Traditional solutions often involve high computational complexity, long processing time, and substantial hardware resource requirements, posing major obstacles to the large-scale deployment of quantum communication and quantum sensing networks. Artificial intelligence (AI), as a major technological advancement in current scientific landscape, offers powerful data processing and analytical capabilities, providing new ideas and methods for optimizing and enhancing quantum communication and sensing systems.Significant progresses have been made in applying AI to quantum communication and sensing, thus injecting new vitality into these cutting-edge technologies. In quantum communication, AI techniques have greatly improved the performance and security of quantum key distribution, quantum memory, and quantum networks through parameter optimization, real-time feedback control, and attack detection. In quantum sensing, quantum sensing technology enables ultra-high sensitivity detection of physical quantities such as time and magnetic fields. The introduction of AI has opened up new avenues for achieving high-precision and high-sensitivity quantum measurements. With AI, sensor performance is optimized, and measurement accuracy is further enhanced through data analysis.This paper also analyzes the current challenges in using AI to empower quantum communication and sensing systems, such as implementing efficient algorithm deployment and system feedback control under limited computational resources, and addressing complex task environments, dynamically changing scenarios, and multi-task coordination requirements. Finally, this paper discusses and envisions future development prospects in this field.

Keywords:

Authors and contacts

1.
Institute of Quantum Information and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
Key Laboratory of Broadband Wireless Communication and Sensor Network of Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Corresponding author: WANG Qin, qinw@njupt.edu.cn

Funds: Project supported by the Industrial Prospect and Key Core Technology Projects of Key R & D Program of Jiangsu Province, China (Grant No. BE2022071), the National Natural Science Foundation of China (Grant Nos. 12074194, 62471248, 62101285), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (Grant No. KYCX23_1030).

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References

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Cited By

图 1 人工智能概述

Figure 1. An overview of artificial intelligence.

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图 2 强化学习示意图

Figure 2. Schematic diagram of reinforcement learning.

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图 3 传统扫描传输程序与使用基于LSTM模型的QKD系统的误码率对比^[29]

Figure 3. Comparisons of QBER between applying traditional scanning-and-transmitting program and using LSTM model for the same QKD system^[29].

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图 4 基于支持向量回归的系统物理参数预测的基本思想^[33]

Figure 4. Basic idea of SVR to solve the physical parameters prediction problem^[33].

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图 5 基于ANN的量子攻击检测模型^[38]　(a)一个没有隐藏层的线性人工神经网络模型, 只能解决线性可分问题; (b)一个带有隐藏层的非线性ANN模型, 用于对不同类型的量子攻击进行分类

Figure 5. ANN-based quantum attack detection model^[38]: (a) A linear ANN model without the hidden layer which can only solve linear separable problems; (b) a nonlinear ANN model with a hidden layer to classify different types of quantum attacks.

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图 6 基于机器学习的攻击检测方案的实现过程^[39]

Figure 6. Implementation process of a machine-learning-based attack detection scheme^[39].

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图 7 机器学习算法的详细流程示意图^[47], 输入数据集(离散化的驱动场)不断地进行调整, 直到输出值和目标值之间的误差(强化学习算法中的反馈信号)降低到足够小的水平

Figure 7. Detailed schematic of the ML algorithm^[47], the input dataset (discrete control laser pulse) iteratively adjusts itself until the error (feedback in training) between the estimated output and the target value becomes small enough.

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图 8 量子密钥分发光网络中基于深度强化学习的路由与资源分配框架示意图^[55]

Figure 8. An illustration of the proposed deep reinforcement learning framework for the routing and resource assignment in quantum key distribution-secured optical networks^[55].

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图 9 即插即用双场量子接入网络与10 G-以太网无源光网络的全共存架构^[56]

Figure 9. Full coexistence architecture of plug-and-play twin-field QAN and 10 G-EPON^[56].

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图 10 深度强化学习在量子传感协议中的应用示意图, I是智能体与环境的交互; II是基于状态感知的策略网络和价值网络, 采用LSTMCells^[77]

Figure 10. Illustration of deep reinforcement learning with (I) agent-environment interaction (II) state-aware policy and value networks with LSTMCells for quantum sensing protocols^[77].

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图 11 基于深度学习的量子传感示意图^[80]

Figure 11. Schematic of deep-learning-based quantum sensing^[80].

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表 1 人工智能在DV-QKD中的应用对比

Table 1. Comparison of artificial intelligence applications in DV-QKD.

应用领域	方法	主要贡献	参考文献
参数优化	随机森林	预测MDI-QKD和BB84-QKD协议的最优参数	[24]
参数优化	神经网络	直接预测QKD系统最优参数	[25]
参数优化	极端梯度提升	预测TF-QKD的优化参数, 效率和准确性优于RF和BPNN	[27]
参数优化和系统校准	反向传播神经网络	预测系统最优参数, 同时通过仿真对MDI-QKD系统进行相位校准	[26]
协议选择和参数优化	随机森林	首次将机器学习方法应用于QKD系统实现最优协议选择与系统参数优化	[28]
相位校准	长短记忆网络	预测设备物理参数, 实时进行BB84-QKD系统的相位校准	[29]
相位校准	长短记忆网络	预测MDI-QKD系统中两个用户的相位漂移, 实时主动补偿	[30]
相位校准	长短记忆网络	预测TF-QKD系统相位漂移, 实现主动反馈控制	[31]
设备缺陷和攻击检测	随机森林	实时检测设备缺陷和攻击, 准确率高达98%	[32]

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表 2 人工智能在CV-QKD中的应用对比

Table 2. Comparison of artificial intelligence applications in CV-QKD.

应用领域	方法	主要贡献	参考文献
参数优化	支持向量回归	预测系统物理参数, 优化QKD系统性能和安全性	[33]
参数优化	反向传播神经网络	调整调制方差, 确保系统安全, 有效地提高了密钥率	[34]
参数优化	机器学习框架	控制相位噪声, 优化调制方差, 实现100 km光纤通道上的密钥分发	[40]
密钥率预测	多标签分类算法	通过多标签分类算法区分相干态, 优于现有离散调制CV-QKD协议	[35]
密钥率预测	神经网络	快速预测离散调制CV-QKD协议的密钥率, 速度和准确性优于传统数值方法	[36,37]
攻击检测	人工神经网络	自动识别和分类攻击类型, 准确率和召回率超过99%	[38]
攻击检测	密度聚类和多类支持向量机	高效检测量子黑客攻击, 修正密钥率高估问题, 提供更紧致的安全边界	[39]

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表 3 人工智能在量子传感中的应用对比

Table 3. Comparison of artificial intelligence applications in quantum sensing.

应用领域	方法	主要贡献	参考文献
量子传感器校准	神经网络	利用神经网络处理训练数据中的不确定性, 实现接近量子极限的测量精度	[73]
参数估计	粒子群优化	自动设计干涉仪相位估计的反馈策略, 精度接近海森伯极限, 优于传统的 BWB策略	[74]
参数估计	强化学习	训练神经网络生成适用于不同参数值的控制序列, 避免每次参数更新时重新优化的高计算成本	[75]
量子传感器优化	强化学习	训练神经网络生成适利用强化学习的交叉熵方法优化量子传感器的动力学特性	[76]
参数估计	深度强化学习	从几何角度推导了参数估计的 QFI 的无噪声和有噪声边界, 在无噪声和有噪声条件下均展现出良好的鲁棒性和样本效率	[77]
参数估计	模型感知强化学习	结合贝叶斯估计和强化学习, 优化量子计量学中的实验设计, 适用于多种量子平台	[78]
未知环境	深度学习	结合图神经网络和三角插值算法, 使光学量子传感器在未知环境中达到海森伯极限精度	[80]
微波探测	深度学习	提出不求解主方程即可有效探测多频率微波电场的方案, 硬件要求低, 精度高	[79]

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[1]	Das Sarma S, Deng D L, Duan L M 2019 Phys. Today 72 48 Google Scholar
[2]	Carleo G, Cirac I, Cranmer K, Daudet L, Schuld M, Tishby N, Vogt-Maranto L, Zdeborová L 2019 Rev. Mod. Phys. 91 045002 Google Scholar
[3]	Wallnöfer J, Melnikov A A, Dür W, Briegel H J 2020 PRX Quantum 1 010301 Google Scholar
[4]	Kaelbling L P, Littman M L, Moore A W 1996 J. Artif. Intell. Res. 4 237 Google Scholar
[5]	LeCun Y, Bengio Y, Hinton G 2015 Nature 521 436 Google Scholar
[6]	Ma W, Liu Z, Kudyshev Z A, Boltasseva A, Cai W, Liu Y 2021 Nat. Photonics 15 77 Google Scholar
[7]	Jain A K, Mao J, Mohiuddin K M 1996 Computer 29 31 Google Scholar
[8]	Gers F A, Schmidhuber J, Cummins F 2000 Neural Comput. 12 2451 Google Scholar
[9]	Cleveland W S, Devlin S J, Grosse E 1988 J. Econom. 37 87 Google Scholar
[10]	Kotsiantis S B 2013 Artif. Intell. Rev. 39 261 Google Scholar
[11]	Snoek J, Larochelle H, Adams R P 2012 Advances in Neural Information Processing Systems 25 (NIPS 2012) Lake Tahoe, USA, December 3–8, 2012 p1
[12]	Xu R, Wunsch D 2005 IEEE Trans. Neural Netw. 16 645 Google Scholar
[13]	Raymer M L, Punch W F, Goodman E D, Kuhn L A, Jain A K 2000 IEEE Trans. Evol. Comput. 4 164 Google Scholar
[14]	Dietterich T G 2000 Proceedings of the First International Workshop on Multiple Classifier Systems June 21–23, 2000 pp1–15
[15]	Bennett C H, Brassard G 1984 Proceeding of the IEEE International Conference on Computers, Systems and Signal Processing Bangalore, 1984 pp175–179
[16]	Ekert A K 1991 Phys. Rev. Lett. 67 661 Google Scholar
[17]	Bennett C H, Brassard G, Mermin N D 1992 Phys. Rev. Lett. 68 557 Google Scholar
[18]	Bennett C H 1992 Phys. Rev. Lett. 68 3121 Google Scholar
[19]	Acín A, Brunner N, Gisin N, Massar S, Pironio S, Scarani V 2007 Phys. Rev. Lett. 98 230501 Google Scholar
[20]	Lo H K, Curty M, Qi B 2012 Phys. Rev. Lett. 108 130503 Google Scholar
[21]	Lucamarini M, Yuan Z L, Dynes J F, Shields A J 2018 Nature 557 400 Google Scholar
[22]	Zeng P, Zhou H, Wu W, Ma X 2022 Nat. Commun. 13 3903 Google Scholar
[23]	Xie Y M, Lu Y S, Weng C X, Cao X Y, Jia Z J, Bao Y, Wang Y, Fu Y, Yin H L, Chen Z B 2022 PRX Quantum 3 020315 Google Scholar
[24]	Ding H J, Liu J Y, Zhang C M, Wang Q 2020 Quantum Inf. Process. 19 1 Google Scholar
[25]	Wang W, Lo H K 2019 Phys. Rev. A 100 062334 Google Scholar
[26]	Lu F Y, Yin Z Q, Wang C, Cui C H, Teng J, Wang S, Chen W, Huang W, Xu B J, Guo G C, Han Z F 2019 J. Opt. Soc. Am. B 36 B92 Google Scholar
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[29]	Liu J Y, Ding H J, Zhang C M, Xie S P, Wang Q 2019 Phys. Rev. Appl. 12 014059 Google Scholar
[30]	Zhang S W, Liu J K, Zhang C, Zhou X, Wang Q 2021 Entropy 23 1242 Google Scholar
[31]	Liu J Y, Jiang Q Q, Ding H J, Ma X, Sun M S, Xu J X, Zhang C H, Xie S P, Li J, Zeng G G, Zhou X Y, Wang Q 2023 Sci. China Inf. Sci. 66 189402 Google Scholar
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[33]	Liu W Q, Huang P, Peng J Y, Fan J P, Zeng G H 2018 Phys. Rev. A 97 022316 Google Scholar
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[35]	Liao Q, Xiao G, Zhong H, Guo Y 2020 New J. Phys. 22 083086 Google Scholar
[36]	Zhou M G, Liu Z P, Liu W B, Li C L, Bai J L, Xue Y R, Fu Y, Yin H L 2022 Sci. Rep. 12 8879 Google Scholar
[37]	Liu Z P, Zhou M G, Liu W B, Li C L, Gu J, Yin H L, Chen Z B 2022 Opt. Express 30 15024 Google Scholar
[38]	Mao Y Y, Huang W T, Zhong H, Wang Y J, Qin H, Guo Y, Huang D 2020 New J. Phys. 22 083073 Google Scholar
[39]	Ding C, Wang S, Wang Y N, Wu Z J, Sun J T, Mao Y Y 2023 Phys. Rev. A 107 062422 Google Scholar
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