人工智能赋能量子通信与量子传感系统

徐佳歆; 徐乐辰; 刘靖阳; 丁华建; 王琴

doi:10.7498/aps.74.20250322

摘要
量子通信和量子传感分别利用量子系统的独特特性, 比如量子态的叠加性或量子纠缠特性等, 能够实现信息论安全的通信以及对物理量的高精度测量. 量子通信和量子传感, 作为当前最接近实用化的两种量子技术, 成为学术界的研究热点. 然而, 这两种技术在走向实用化的过程中也面临着诸多挑战, 例如: 设备缺陷导致现实安全性问题, 环境噪声干扰大导致测量精度降低等, 使得系统的大规模部署受到严重限制. 人工智能凭借其强大的算力和数据处理能力, 已经在通信、计算和成像等领域发挥着重要作用. 本文首先综述了人工智能与量子通信和量子传感交叉领域的发展现状, 包括人工智能在量子密钥分发、量子存储、量子网络、量子传感等方向的具体结合与应用, 为提升系统的可靠性、安全性、智能化与可扩展性等方面提供了强有力的保障. 接着分析了人工智能在赋能量子通信和量子传感系统中目前存在的问题, 最后对该领域未来的发展前景进行了展望和讨论.

关键词:
人工智能 /

机器学习 /

量子通信 /

量子传感
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:
artificial intelligence /

machine learning /

quantum communication /

quantum sensing
文章全文

施引文献

图 1 人工智能概述

Fig. 1. An overview of artificial intelligence.

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图 2 强化学习示意图

Fig. 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^[28]

Fig. 3. Comparisons of QBER between applying traditional scanning-and transmitting program and using LSTM model for the same QKD system^[28].

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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^[32]

Fig. 4. Basic idea of SVR to solve the physical parameters prediction problem^[32].

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图 5 基于ANN的量子攻击检测模型^[37]　(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

Fig. 5. ANN-based quantum attack detection model^[37]: (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^[38]

Fig. 6. Implementation process of a machine-learning-based attack detection scheme^[38].

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图 7 机器学习算法的详细流程示意图^[46], 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

Fig. 7. Detailed schematic of the ML algorithm^[46], 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^[54]

Fig. 8. An illustration of the proposed deep reinforcement learning framework for the routing and resource assignment in quantum key distribution‐secured optical networks^[54].

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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^[55]

Fig. 9. Full coexistence architecture of plug-and-play twin-field QAN and 10 G-EPON^[55].

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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LSTMCells57^[77]

Fig. 10. Illustration of DRL with (I) agent-environment interaction (II) state-aware policy and value networks with LSTMCells57 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]

Fig. 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, 同时实现了大规模MDI-QKD网络中的实时校准	[26]
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	[28]
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	[29]
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	[30]
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%	[31]

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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	[32]
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	[33]
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公里光纤通道上的密钥分发	[39]
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	[34]
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	[35,36]
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%	[37]
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	[38]

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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	[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	[78]
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	[79]
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	[75]

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人工智能赋能量子通信与量子传感系统

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

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人工智能赋能量子通信与量子传感系统

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

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