# Anomaly Detection for Real-World Cyber-Physical Security using Quantum Hybrid Support Vector Machines

arXiv: https://arxiv.org/abs/2409.04935 · HTML: https://mlsanity.com/paper/2409.04935/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 8 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Robust data encodings for quantum classifiers](https://mlsanity.com/paper/2003.01695/) (2020)
- [Quantum anomaly detection in the latent space of proton collision events at the LHC](https://mlsanity.com/paper/2301.10780/) (2023)
- [Unravelling physics beyond the standard model with classical and quantum anomaly detection](https://mlsanity.com/paper/2301.10787/) (2023)
- [Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel](https://mlsanity.com/paper/2308.00583/) (2023)
- [Simultaneous execution of quantum circuits on current and near-future NISQ systems](https://mlsanity.com/paper/2112.07091/) (2021)

## Built on by (most cited first, 5 of 5)

- [Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations](https://mlsanity.com/paper/2506.14795/) (2025)
- [Quantum Gated Recurrent GAN with Gaussian Uncertainty for Network Anomaly Detection](https://mlsanity.com/paper/2510.26487/) (2025)
- [Quantum Hybrid Support Vector Machines for Stress Detection in Older Adults](https://mlsanity.com/paper/2501.04831/) (2025)
- [Detection of Physiological Data Tampering Attacks with Quantum Machine Learning](https://mlsanity.com/paper/2502.05966/) (2025)
- [Quantum-Hybrid Support Vector Machines for Anomaly Detection in Industrial Control Systems](https://mlsanity.com/paper/2506.17824/) (2025)

## Abstract (arXiv)

Cyber-physical control systems are critical infrastructures designed around highly responsive feedback loops that are measured and manipulated by hundreds of sensors and controllers. Anomalous data, such as from cyber-attacks, greatly risk the safety of the infrastructure and human operators. With recent advances in the quantum computing paradigm, the application of quantum in anomaly detection can greatly improve identification of cyber-attacks in physical sensor data. In this paper, we explore the use of strong pre-processing methods and a quantum-hybrid Support Vector Machine (SVM) that takes advantage of fidelity in parameterized quantum circuits to efficiently and effectively flatten extremely high dimensional data. Our results show an F-1 Score of 0.86 and accuracy of 87% on the HAI CPS dataset using an 8-qubit, 16-feature quantum kernel, performing equally to existing work and 14% better than its classical counterpart.