Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection

Abstract--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 (ibm fez)--demonstrating practical quantum advantage on current NISQ devices. This work establishes quantum machine learning as a viable, hardware-ready solution for real-world cybersecurity challenges.