Quantum Gated Recurrent GAN with Gaussian Uncertainty for Network Anomaly Detection

• URL: http://arxiv.org/abs/2510.26487v1
• Date: Thu, 30 Oct 2025 13:39:44 GMT
• Title: Quantum Gated Recurrent GAN with Gaussian Uncertainty for Network Anomaly Detection
• Authors: Wajdi Hammami, Soumaya Cherkaoui, Jean-Frederic Laprade, Ola Ahmad, Shengrui Wang,
• Abstract summary: Anomaly detection in time-series data is a critical challenge with significant implications for network security.<n>Recent quantum machine learning approaches have shown promise in capturing complex data distributions for anomaly detection but remain constrained by limited qubit counts.<n>We introduce in this work a novel Quantum Gated Recurrent Unit (QGRU)-based Generative Adversarial Network (GAN) for robust network anomaly detection.
• Score: 10.435718832887988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• Quantum Machine Learning Approaches for Coordinated Stealth Attack Detection in Distributed Generation Systems [0.0]
  Coordinated stealth attacks are a serious cybersecurity threat to distributed generation systems.<n>This study investigates quantum machine learning approaches for detecting coordinated stealth attacks on a distributed generation unit in a microgrid.
  arXiv  Detail & Related papers  (2025-12-30T18:32:13Z)
• Towards Quantum Enhanced Adversarial Robustness with Rydberg Reservoir Learning [45.92935470813908]
  Quantum computing reservoir (QRC) leverages the high-dimensional, nonlinear dynamics inherent in quantum many-body systems.<n>Recent studies indicate that perturbation quantums based on variational circuits remain susceptible to adversarials.<n>We investigate the first systematic evaluation of adversarial robustness in a QR based learning model.
  arXiv  Detail & Related papers  (2025-10-15T12:17:23Z)
• Calibration of Quantum Devices via Robust Statistical Methods [45.464983015777314]
  We numerically analyze advanced statistical methods for Bayesian inference against the state-of-the-art in quantum parameter learning.<n>We show advantages of these approaches over existing ones, namely under multi-modality and high dimensionality.<n>Our findings have applications in challenging quantumcharacterization tasks namely learning the dynamics of open quantum systems.
  arXiv  Detail & Related papers  (2025-07-09T15:22:17Z)
• Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series [6.576759206183036]
  We introduce a quantum generative adversarial network (QGAN) architecture for anomaly detection.<n>By integrating data re-uploading and SuDaI, the approach maps classical data into quantum states efficiently.<n>The QGAN achieves a accuracy high along with high recall and F1-scores in anomaly detection, and attains a lower MSE compared to the classical model.
  arXiv  Detail & Related papers  (2025-05-16T18:47:42Z)
• Quantum-assisted anomaly detection with multivariate Gaussian distribution [1.752215225501591]
  Gassian anomaly detection (GAD) is a prominent task in data mining and machine learning.<n>We propose a quantum algorithm for GAD biult on arithmetic-free black-box quantum state preparation (AFQSP)<n>Our algorithm achieves exponential speedup over the classical GAD when handling low-dimensional datasets.
  arXiv  Detail & Related papers  (2025-05-05T02:21:11Z)
• Bayesian Quantum Amplitude Estimation [46.03321798937855]
  We present BAE, a problem-tailored and noise-aware Bayesian algorithm for quantum amplitude estimation.<n>In a fault tolerant scenario, BAE is capable of saturating the Heisenberg limit; if device noise is present, BAE can dynamically characterize it and self-adapt.<n>We propose a benchmark for amplitude estimation algorithms and use it to test BAE against other approaches.
  arXiv  Detail & Related papers  (2024-12-05T18:09:41Z)
• Mutual information maximizing quantum generative adversarial networks [9.391818870557545]
  InfoQGAN is a quantum-classical hybrid generative adversarial network that integrates the principles of InfoGAN with a QGAN architecture.<n>We show that InfoQGAN effectively mitigates mode collapse while achieving robust feature disentanglement in the quantum generator.<n>These results highlight the potential of InfoQGAN as an approach for advancing quantum generative modeling in the NISQ era.
  arXiv  Detail & Related papers  (2023-09-04T05:18:37Z)
• Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection [3.955274213382716]
  Anomaly detection in Restricted Detection and Response (EDR) is a critical task in cybersecurity programs of large companies. Classical machine learning approaches to this problem exist, but they frequently show unsatisfactory performance in differentiating malicious from benign anomalies. A promising approach to attain superior generalization than currently employed machine learning techniques are quantum generative models.
  arXiv  Detail & Related papers  (2023-06-08T07:36:01Z)
• Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning [47.991114317813555]
  Quantum models implement implicit probabilistic predictors that produce multiple random decisions for each input through measurement shots. This paper proposes to leverage such randomness to define prediction sets for both classification and regression that provably capture the uncertainty of the model.
  arXiv  Detail & Related papers  (2023-04-06T22:05:21Z)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• Entangling Quantum Generative Adversarial Networks [53.25397072813582]
  We propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor.
  arXiv  Detail & Related papers  (2021-04-30T20:38:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.