# Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection

arXiv: https://arxiv.org/abs/2511.21932 · HTML: https://mlsanity.com/paper/2511.21932/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 26 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators](https://mlsanity.com/paper/2505.17756/) (2025)
- [Modeling Feature Maps for Quantum Machine Learning](https://mlsanity.com/paper/2501.08205/) (2025)
- [Adapting Quantum Machine Learning for Energy Dissociation of Bonds](https://mlsanity.com/paper/2510.06563/) (2025)

## Abstract (arXiv)

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.