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HMB-GAN: Hybrid Multi-B'ezier GAN for Vector Shape Synthesis

arXiv 2609.21158 ↗First posted 17 Sep 2026cs.LGcited by 0

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Abstract (arXiv)

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-B'ezier domains, we introduce HMB-GAN (Hybrid Multi-B'ezier GAN), an end-to-end differentiable generative framework that constructs closed shapes through stitched multi-segment B'ezier 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.

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References · 18

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6 more
  1. 2020SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Designcited by 72
  2. 2020Airfoil Design Parameterization and Optimization using B'ezier Generative Adversarial Networkscited by 35
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  6. 2023A hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloudcited by 3

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