# Q-AGNN: Quantum-Enhanced Attentive Graph Neural Network for Intrusion Detection

arXiv: https://arxiv.org/abs/2603.22365 · HTML: https://mlsanity.com/paper/2603.22365/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 23 Mar 2026.

## Builds on (most cited first, 15 of 15)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset](https://mlsanity.com/paper/1811.00701/) (2018)
- [Quantum Graph Neural Networks](https://mlsanity.com/paper/1909.12264/) (2019)
- [E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT](https://mlsanity.com/paper/2103.16329/) (2021)
- [NetFlow Datasets for Machine Learning-based Network Intrusion Detection Systems](https://mlsanity.com/paper/2011.09144/) (2020)
- [Financial Fraud Detection using Quantum Graph Neural Networks](https://mlsanity.com/paper/2309.01127/) (2023)
- [Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks](https://mlsanity.com/paper/2207.06819/) (2022)
- [How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing](https://mlsanity.com/paper/2405.12474/) (2024)
- [Quantum Graph Convolutional Neural Networks](https://mlsanity.com/paper/2107.03257/) (2021)
- [A Piece-wise Polynomial Filtering Approach for Graph Neural Networks](https://mlsanity.com/paper/2112.03499/) (2021)
- [Quantum Graph Attention Network: A Novel Quantum Multi-Head Attention Mechanism for Graph Learning](https://mlsanity.com/paper/2508.17630/) (2025)

## Built on by (most cited first, 2 of 2)

- [How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection](https://mlsanity.com/paper/2608.18155/) (2026)
- [Impact of Data Loss in Postprocessing on Training and Inference of Quantum Neural Networks](https://mlsanity.com/paper/2609.05060/) (2026)

## Abstract (arXiv)

With the rapid growth of interconnected devices, accurately detecting malicious activities in network traffic has become increasingly challenging. Most existing deep learning-based intrusion detection systems treat network flows as independent instances, thereby failing to exploit the relational dependencies inherent in network communications. To address this limitation, we propose Q-AGNN, a Quantum-Enhanced Attentive Graph Neural Network for intrusion detection, where network flows are modeled as nodes and edges represent similarity relationships. Q-AGNN leverages parameterized quantum circuits (PQCs) to encode multi-hop neighborhood information into a high-dimensional latent space, inducing a bounded quantum feature map that implements a second-order polynomial graph filter in a quantum-induced Hilbert space. An attention mechanism is subsequently applied to adaptively weight the quantum-enhanced embeddings, allowing the model to focus on the most influential nodes contributing to anomalous behavior. Extensive experiments conducted on four benchmark intrusion detection datasets demonstrate that Q-AGNN achieves competitive or superior detection performance compared to state-of-the-art graph-based methods, while consistently maintaining low false positive rates under hardware-calibrated noise conditions. Moreover, we also executed the Q-AGNN framework on actual IBM quantum hardware to demonstrate the practical operability of the proposed pipeline under real NISQ conditions. These results highlight the effectiveness of integrating quantum-enhanced representations with attention mechanisms for graph-based intrusion detection and underscore the potential of hybrid quantum-classical learning frameworks in cybersecurity applications.