# Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series

arXiv: https://arxiv.org/abs/2505.11631 · HTML: https://mlsanity.com/paper/2505.11631/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 14 of 14)

- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [A review on outlier/anomaly detection in time series data](https://mlsanity.com/paper/2002.04236/) (2020)
- [Experimental Quantum Generative Adversarial Networks for Image Generation](https://mlsanity.com/paper/2010.06201/) (2020)
- [Quantum Data Encoding: A Comparative Analysis of Classical-to-Quantum Mapping Techniques and Their Impact on Machine Learning Accuracy](https://mlsanity.com/paper/2311.10375/) (2023)
- [Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Services](https://mlsanity.com/paper/2202.06435/) (2022)
- [Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks](https://mlsanity.com/paper/2206.11328/) (2022)
- [User Trajectory Prediction in Mobile Wireless Networks Using Quantum Reservoir Computing](https://mlsanity.com/paper/2301.08796/) (2023)
- [Successive Data Injection in Conditional Quantum GAN Applied to Time Series Anomaly Detection](https://mlsanity.com/paper/2310.05307/) (2023)
- [QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecasting](https://mlsanity.com/paper/2408.12007/) (2024)
- [LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder](https://mlsanity.com/paper/2409.14622/) (2024)
- [Open RAN Slicing with Quantum Optimization](https://mlsanity.com/paper/2502.00142/) (2025)
- [MistralBSM: Leveraging Mistral-7B for Vehicular Networks Misbehavior Detection](https://mlsanity.com/paper/2407.18462/) (2024)
- [Prediction and Detection of FDIA and DDoS Attacks in 5G Enabled IoT](https://mlsanity.com/paper/2201.11368/) (2022)

## Built on by (most cited first, 3 of 3)

- [Quantum Gated Recurrent GAN with Gaussian Uncertainty for Network Anomaly Detection](https://mlsanity.com/paper/2510.26487/) (2025)
- [QFed: Parameter-Compact Quantum-Classical Federated Learning](https://mlsanity.com/paper/2601.09809/) (2026)
- [Hybrid Quantum-Classical GANs for the Generation of Adversarial Network Flows](https://mlsanity.com/paper/2605.06629/) (2026)

## Abstract (arXiv)

Quantum computing may offer new approaches for advancing machine learning, including in complex tasks such as anomaly detection in network traffic. In this paper, we introduce a quantum generative adversarial network (QGAN) architecture for multivariate time-series anomaly detection that leverages variational quantum circuits (VQCs) in combination with a time-window shifting technique, data re-uploading, and successive data injection (SuDaI). The method encodes multivariate time series data as rotation angles. By integrating both data re-uploading and SuDaI, the approach maps classical data into quantum states efficiently, helping to address hardware limitations such as the restricted number of available qubits. In addition, the approach employs an anomaly scoring technique that utilizes both the generator and the discriminator output to enhance the accuracy of anomaly detection. The QGAN was trained using the parameter shift rule and benchmarked against a classical GAN. Experimental results indicate that the quantum model achieves a accuracy high along with high recall and F1-scores in anomaly detection, and attains a lower MSE compared to the classical model. Notably, the QGAN accomplishes this performance with only 80 parameters, demonstrating competitive results with a compact architecture. Tests using a noisy simulator suggest that the approach remains effective under realistic noise-prone conditions.