# Quantum Gated Recurrent GAN with Gaussian Uncertainty for Network Anomaly Detection

arXiv: https://arxiv.org/abs/2510.26487 · HTML: https://mlsanity.com/paper/2510.26487/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 12 of 12)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Precision and Recall for Time Series](https://mlsanity.com/paper/1803.03639/) (2018)
- [IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection](https://mlsanity.com/paper/1809.02077/) (2018)
- [DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN](https://mlsanity.com/paper/1808.07632/) (2018)
- [A Survey on GANs for Anomaly Detection](https://mlsanity.com/paper/1906.11632/) (2019)
- [PATE: Proximity-Aware Time series anomaly Evaluation](https://mlsanity.com/paper/2405.12096/) (2024)
- [Successive Data Injection in Conditional Quantum GAN Applied to Time Series Anomaly Detection](https://mlsanity.com/paper/2310.05307/) (2023)
- [Anomaly Detection for Real-World Cyber-Physical Security using Quantum Hybrid Support Vector Machines](https://mlsanity.com/paper/2409.04935/) (2024)
- [Enhancing Network Intrusion Detection Performance using Generative Adversarial Networks](https://mlsanity.com/paper/2404.07464/) (2024)
- [TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial Networks](https://mlsanity.com/paper/2303.12952/) (2023)
- [Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series](https://mlsanity.com/paper/2505.11631/) (2025)

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

- [Hybrid Quantum-Classical GANs for the Generation of Adversarial Network Flows](https://mlsanity.com/paper/2605.06629/) (2026)

## Abstract (arXiv)

Anomaly detection in time-series data is a critical challenge with significant implications for network security. Recent quantum machine learning approaches, such as quantum kernel methods and variational quantum circuits, have shown promise in capturing complex data distributions for anomaly detection but remain constrained by limited qubit counts. We introduce in this work a novel Quantum Gated Recurrent Unit (QGRU)-based Generative Adversarial Network (GAN) employing Successive Data Injection (SuDaI) and a multi-metric gating strategy for robust network anomaly detection. Our model uniquely utilizes a quantum-enhanced generator that outputs parameters (mean and log-variance) of a Gaussian distribution via reparameterization, combined with a Wasserstein critic to stabilize adversarial training. Anomalies are identified through a novel gating mechanism that initially flags potential anomalies based on Gaussian uncertainty estimates and subsequently verifies them using a composite of critic scores and reconstruction errors. Evaluated on benchmark datasets, our method achieves a high time-series aware F1 score (TaF1) of 89.43% demonstrating superior capability in detecting anomalies accurately and promptly as compared to existing classical and quantum models. Furthermore, the trained QGRU-WGAN was deployed on real IBM Quantum hardware, where it retained high anomaly detection performance, confirming its robustness and practical feasibility on current noisy intermediate-scale quantum (NISQ) devices.