# Quantum Machine Learning for UAV Swarm Intrusion Detection

arXiv: https://arxiv.org/abs/2509.01812 · HTML: https://mlsanity.com/paper/2509.01812/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Builds on (most cited first, 19 of 19)

- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Challenges and Opportunities in Quantum Machine Learning](https://mlsanity.com/paper/2303.09491/) (2023)
- [Variational Quantum Circuits for Deep Reinforcement Learning](https://mlsanity.com/paper/1907.00397/) (2019)
- [Quantum Long Short-Term Memory](https://mlsanity.com/paper/2009.01783/) (2020)
- [Federated Quantum Machine Learning](https://mlsanity.com/paper/2103.12010/) (2021)
- [Challenges and Opportunities in Quantum Optimization](https://mlsanity.com/paper/2312.02279/) (2023)
- [Towards provably efficient quantum algorithms for large-scale machine-learning models](https://mlsanity.com/paper/2303.03428/) (2023)
- [Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges](https://mlsanity.com/paper/1809.01752/) (2018)
- [Validating Large-Scale Quantum Machine Learning: Efficient Simulation of Quantum Support Vector Machines Using Tensor Networks](https://mlsanity.com/paper/2405.02630/) (2024)
- [Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2412.08845/) (2024)
- [CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data](https://mlsanity.com/paper/2409.08584/) (2024)
- [Quantum Kernel-Based Long Short-term Memory](https://mlsanity.com/paper/2411.13225/) (2024)
- [Consensus-based Distributed Quantum Kernel Learning for Speech Recognition](https://mlsanity.com/paper/2409.05770/) (2024)
- [Robust Decentralized Quantum Kernel Learning for Noisy and Adversarial Environment](https://mlsanity.com/paper/2504.13782/) (2025)
- [Federated Learning for UAV Swarms Under Class Imbalance and Power Consumption Constraints](https://mlsanity.com/paper/2108.10748/) (2021)
- [Intrusion Detection Systems for Networked Unmanned Aerial Vehicles: A Survey](https://mlsanity.com/paper/1807.00435/) (2018)
- [SwarmControl: An Automated Distributed Control Framework for Self-Optimizing Drone Networks](https://mlsanity.com/paper/2005.09781/) (2020)
- [Resource-Efficient Compilation of Distributed Quantum Circuits for Solving Large-Scale Wireless Communication Network Problems](https://mlsanity.com/paper/2501.10242/) (2025)
- [Towards Secure Wireless Mesh Networks for UAV Swarm Connectivity: Current Threats, Research, and Opportunities](https://mlsanity.com/paper/2108.13154/) (2021)

## Built on by (most cited first, 1 of 1)

- [Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series](https://mlsanity.com/paper/2511.02301/) (2025)

## Abstract (arXiv)

Intrusion detection in unmanned-aerial-vehicle (UAV) swarms is complicated by high mobility, non-stationary traffic, and severe class imbalance. Leveraging a 120 k-flow simulation corpus that covers five attack types, we benchmark three quantum-machine-learning (QML) approaches - quantum kernels, variational quantum neural networks (QNNs), and hybrid quantum-trained neural networks (QT-NNs) - against strong classical baselines. All models consume an 8-feature flow representation and are evaluated under identical preprocessing, balancing, and noise-model assumptions. We analyse the influence of encoding strategy, circuit depth, qubit count, and shot noise, reporting accuracy, macro-F1, ROC-AUC, Matthews correlation, and quantum-resource footprints. Results reveal clear trade-offs: quantum kernels and QT-NNs excel in low-data, nonlinear regimes, while deeper QNNs suffer from trainability issues, and CNNs dominate when abundant data offset their larger parameter count. The complete codebase and dataset partitions are publicly released to enable reproducible QML research in network security.