通过神经噪声编码和强度校准的端到端QGAN图像合成
End-to-End QGAN-Based Image Synthesis via Neural Noise Encoding and Intensity Calibration
AI总结：
本文提出ReQGAN框架，利用单个D-qubit量子电路生成完整图像，解决量子生成器角色弱化和全局语义捕捉难题，通过神经噪声编码和强度校准模块实现稳定训练和有效图像合成。
摘要：
量子生成对抗网络（QGANs）为在近期量子设备上学习数据分布提供了有前途的路径。然而，现有的图像合成QGANs避免直接生成完整图像，依赖经典后处理或基于块的方法。这些方法稀释了量子生成器的作用，并难以捕捉全局图像语义。为了解决这个问题，我们提出了ReQGAN，一个端到端框架，利用单个D-qubit量子电路生成完整N=2^D像素图像。ReQGAN克服了阻碍直接像素生成的两个基本瓶颈：（1）刚性的经典到量子噪声接口和（2）归一化量子统计与所需像素强度空间之间的输出不匹配。我们引入了可学习的神经噪声编码器用于自适应状态准备，以及可微的强度校准模块将测量映射到稳定且具有视觉意义的像素域。在MNIST和Fashion-MNIST上的实验表明，ReQGAN在严格的量子位预算下实现了稳定的训练和有效的图像合成，消融研究验证了每个组件的贡献。
英文摘要：
Quantum Generative Adversarial Networks (QGANs) offer a promising path for learning data distributions on near-term quantum devices. However, existing QGANs for image synthesis avoid direct full-image generation, relying on classical post-processing or patch-based methods. These approaches dilute the quantum generator's role and struggle to capture global image semantics. To address this, we propose ReQGAN, an end-to-end framework that synthesizes an entire N=2^D-pixel image using a single D-qubit quantum circuit. ReQGAN overcomes two fundamental bottlenecks hindering direct pixel generation: (1) the rigid classical-to-quantum noise interface and (2) the output mismatch between normalized quantum statistics and the desired pixel-intensity space. We introduce a learnable Neural Noise Encoder for adaptive state preparation and a differentiable Intensity Calibration module to map measurements to a stable, visually meaningful pixel domain. Experiments on MNIST and Fashion-MNIST demonstrate that ReQGAN achieves stable training and effective image synthesis under stringent qubit budgets, with ablation studies verifying the contribution of each component.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
