用类量子生成模型提升医学影像生成质量与效率
MediQ-GAN: Quantum-Inspired GAN for High Resolution Medical Image Generation
- 设计双流生成器融合经典与类量子分支，避免梯度消失
- 在三个医学数据集上超越现有GAN与扩散模型性能
- 提供首个潜空间几何与秩分析，适合医疗数据增强研究者
机器学习辅助诊断前景广阔，但医学影像数据常稀缺、不平衡且受隐私限制，数据增强至关重要。传统生成模型通常需要大量计算与样本资源。量子计算提供新路径，但现有基于量子的图像生成方法规模有限且易遇平庸悬崖问题。我们提出MediQ-GAN，一种带有原型引导跳跃连接的类量子生成对抗网络，其双流生成器融合经典与类量子分支。变分量子电路固有地保持满秩映射，避免秩坍缩，并理论指导以平衡表达力与可训练性。除生成质量外，我们首次对类量子GAN进行潜空间几何与秩分析，揭示其性能机制。在三个医学影像数据集上，MediQ-GAN优于当前最优的GAN与扩散模型。虽在IBM硬件上验证鲁棒性，本工作为硬件无关设计，提供可扩展、数据高效的医学图像生成与增强框架。
原文摘要 · Abstract (English)
Machine learning-assisted diagnosis shows promise, yet medical imaging datasets are often scarce, imbalanced, and constrained by privacy, making data augmentation essential. Classical generative models typically demand extensive computational and sample resources. Quantum computing offers a promising alternative, but existing quantum-based image generation methods remain limited in scale and often face barren plateaus. We present MediQ-GAN, a quantum-inspired GAN with prototype-guided skip connections and a dual-stream generator that fuses classical and quantum-inspired branches. Its variational quantum circuits inherently preserve full-rank mappings, avoid rank collapse, and are theory-guided to balance expressivity with trainability. Beyond generation quality, we provide the first latent-geometry and rank-based analysis of quantum-inspired GANs, offering theoretical insight into their performance. Across three medical imaging datasets, MediQ-GAN outperforms state-of-the-art GANs and diffusion models. While validated on IBM hardware for robustness, our contribution is hardware-agnostic, offering a scalable and data-efficient framework for medical image generation and augmentation.
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