用量子自编码器压缩物联网流量，实现高效异常检测。
Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection
- 构建量子自编码器压缩流量数据，生成可区分的潜在特征。
- 在理想模拟和IBM量子硬件上均实现高精度检测，证明当前量子设备优势。
- 噪声反而提升模型泛化能力，适合实际部署的量子安全应用。
日益增长的网络威胁与物联网流量的高维复杂性已超出传统异常检测方法的能力。尽管深度学习有所改进，但计算瓶颈限制了其大规模实时部署。本文提出一种量子自编码器（QAE）框架，将网络流量压缩为具有判别性的潜在表示，并采用量子支持向量分类（QSVC）进行入侵检测。在三个数据集上的评估表明，该方法在理想模拟器和IBM量子硬件上均取得优异准确率，证明了在当前NISQ设备上的实际量子优势。关键发现是，适度的去极化噪声起到隐式正则化作用，稳定训练过程并增强泛化性能。本工作确立了量子机器学习作为可直接部署于真实世界网络安全挑战的可行方案。
原文摘要 · Abstract (English)
Escalating cyber threats and the high-dimensional complexity of IoT traffic have outpaced classical anomaly detection methods. While deep learning offers improvements, computational bottlenecks limit real-time deployment at scale. We present a quantum autoencoder (QAE) framework that compresses network traffic into discriminative latent representations and employs quantum support vector classification (QSVC) for intrusion detection. Evaluated on three datasets, our approach achieves improved accuracy on ideal simulators and on the IBM Quantum hardware demonstrating practical quantum advantage on current NISQ devices. Crucially, moderate depolarizing noise acts as implicit regularization, stabilizing training and enhancing generalization. This work establishes quantum machine learning as a viable, hardware-ready solution for real-world cybersecurity challenges.
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