HMB-GAN: Hybrid Multi-Bézier GAN for Vector Shape Synthesis

Elian Hugh Thiele-Evans; Binh Duong Pham; Hani Omar M Alharbi; Liibaan Aaden; Syed Umer Hasnain Zaidi; Prem Prakash Jayaraman; Muhammad Saeed; Boris Eisenbart

cs.LGSep 17, 2026

HMB-GAN: Hybrid Multi-Bézier GAN for Vector Shape Synthesis

Authors: Elian Hugh Thiele-Evans, Binh Duong Pham, Hani Omar M Alharbi, Liibaan Aaden, Syed Umer Hasnain Zaidi, Prem Prakash Jayaraman, Muhammad Saeed, Boris Eisenbart

Organizations: School of Science, Computing and Emerging Technologies Swinburne University of Technology Melbourne, Australia · School of Engineering, and School of Design and Architecture Swinburne University of Technology Melbourne, Australia

Abstract

We explore the use of hybrid quantum-classical generative adversarial networks for synthesising CAD-ready vector geometries. Unlike prior work that operates in rasterised or single-Bézier domains, we introduce HMB-GAN (Hybrid Multi-Bézier GAN), an end-to-end differentiable generative framework that constructs closed shapes through stitched multi-segment Bézier representations with geometric continuity enforced by construction. We compare a quantum-enhanced generator with a classical generator within this architecture and evaluate them across point cloud distribution metrics and geometric shape statistics. Results show that despite faster convergence, a reduction in model parameter count, and slightly improved performance on point cloud metrics, the quantum generator suffers from excessive simulator overhead and thus classically-simulated evaluation suffers from hardware constraints. These results demonstrate the feasibility of modelling structured geometries through hybrid quantum architectures whilst highlighting contemporary hardware limitations.

Generative Adversarial Networks

Generative Adversarial Networks (GANs) train competing generators and discriminators to synthesize data, translate images, and model complex distributions.

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Hybrid Quantum-Classical GANs for the Generation of Adversarial Network Flows

Prateek Paudel, Nitin Jha, Abhishek Parakh, Mahadevan Subramaniam

Kennesaw State University, Marietta, GA, USA · University of Nebraska Omaha, Omaha, NE, USA

Adversarial Attacks Network Intrusion Detection Hybrid Quantum-Classical ML Generative Adversarial Networks

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Classical generative adversarial networks (GANs) have been applied to generate adversarial network traffic capable of attacking intrusion detection systems, but they suffer from shortcomings such as the need for large amounts of high-dimensional datasets, mode collapse, and high computational overhead. In this work, we propose a hybrid quantum-classical GAN (QC-GAN) framework where a variational quantum generator is used to generate synthetic network traffic flows mimicking malicious traffic using latent representations. Instead of sampling classical noise vectors, we encode the latent vector (the hidden features) as a quantum state, which is the basis for claiming more expressive latent representations and reducing computational overhead. A classical discriminator will be trained on real-world datasets (UNSW-NB15) and the proposed QC-GAN-generated fake network flows. In this configuration, the generator aims to minimize the discriminator's ability to distinguish real from fake traffic, while the discriminator aims to maximize its classification accuracy, in an iterative manner. In our attack model, we assume that the attacker is a state actor with access to limited quantum computing power, whereas the discriminator is chosen to be classical, as will likely be the case for most end users and organizations. We test the generated flows using classical intrusion detection system (IDS) models, such as a random forest classifier and a convolutional neural network-based classifier, for their ability to bypass the detection process. This work aims to highlight the possibilities of quantum machine learning as a means of generating advanced attack flows and stress testing classical IDS. Lastly, we further evaluate how hardware-based noise affects these attacks to offer a new perspective on IDS, highlighting the need for a quantum resilient defense system.

Abstract

Generative Adversarial Networks

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