发表机构
Swinburne University of Technology（斯威本科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出HMB-GAN，一种混合量子-经典生成对抗网络，用于合成CAD矢量几何形状，通过多段贝塞尔表示和构造性几何连续性，实验表明量子生成器虽收敛快、参数少，但受模拟器开销限制，验证了混合量子架构的可行性。
AI 中文摘要
我们探索了使用混合量子-经典生成对抗网络来合成可直接用于CAD的矢量几何图形。与先前在光栅化或单贝塞尔域中开展的工作不同，我们提出了HMB-GAN（混合多贝塞尔生成对抗网络），这是一个端到端可微的生成框架，通过拼接多段贝塞尔表示来构建闭合形状，并通过构造方式强制实现几何连续性。我们在此架构中比较了量子增强生成器与经典生成器，并使用点云分布指标和几何形状统计量对它们进行评估。结果表明，尽管量子生成器收敛更快、模型参数数量更少，且在点云指标上表现略有提升，但其遭受了过高的模拟器开销，因此经典模拟评估受到硬件限制。这些结果证明了通过混合量子架构建模结构化几何图形的可行性，同时也凸显了当前硬件条件的局限性。
英文摘要
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.
Comments7 pages, 2 figures, 3 tables. Published in the 2026 IEEE International Conference on Quantum Software (QSW)
Journal refE. H. Thiele-Evans et al., "HMB-GAN: Hybrid Multi-Bézier GAN for Vector Shape Synthesis", 2026 IEEE International Conference on Quantum Software (QSW), pp. 222-228, 2026
DOI：10.1109/QSW72780.2026.00033
