Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection

TL;DR

This paper introduces a quantum autoencoder framework combined with quantum support vector classification for IoT anomaly detection, demonstrating improved accuracy and practical quantum advantage on current quantum hardware, with noise aiding training stability.

Contribution

The paper presents a novel quantum autoencoder and QSVC approach for IoT anomaly detection, showing real-world applicability and benefits of noise as regularization.

Findings

Achieved improved accuracy on simulated and real quantum hardware.

Demonstrated practical quantum advantage on NISQ devices.

Moderate noise acts as implicit regularization, stabilizing training.

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 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.