Problem
Research questions and friction points this paper is trying to address.
Hybrid Quantum-Classical GAN
Vector Shape Synthesis
CAD-Ready Geometries
Multi-Bézier Representations
HMB-GAN: Hybrid Multi-Bézier GAN for Vector Shape Synthesis
🤖 AI Summary
研究使用混合量子-经典生成对抗网络合成CAD就绪矢量几何，通过多段贝塞尔表示构建闭合形状，但量子生成器受限于硬件。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hybrid Multi-Bézier GAN
CAD-ready vector geometries
quantum-enhanced generator
geometric continuity
🔎 Similar Papers
No similar papers found.
