用混沌哈密顿量实现量子数据生成，更稳定且适合模拟硬件。
Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model
- 用混沌哈密顿量代替随机电路，实现量子态扩散。
- 在多种模拟量子平台上训练更稳定，性能接近传统方法。
- 适合资源受限的模拟量子设备，提升生成模型实用性。
量子数据生成模型在化学信息学和量子物理等领域具有巨大潜力，但面临严峻挑战。量子去噪扩散概率模型（QuDDPM）通过逐步混淆与去噪量子态实现高效学习，但现有方法依赖电路级随机酉演化，成本高且易受控制误差影响，尤其在模拟量子硬件上表现不佳。本文提出混沌量子扩散模型，利用混沌哈密顿量时间演化生成投影系综，提供灵活且硬件兼容的扩散机制。该方法仅需全局、时间不变的控制，显著降低各类模拟量子平台的实现开销，同时达到与QuDDPM相当的精度。该方法提升了训练稳定性和鲁棒性，拓宽了量子生成建模的应用范围。
原文摘要 · Abstract (English)
Generative models for quantum data pose significant challenges but hold immense potential in fields such as chemoinformatics and quantum physics. Quantum denoising diffusion probabilistic models (QuDDPMs) enable efficient learning of quantum data distributions by progressively scrambling and denoising quantum states; however, existing implementations typically rely on circuit-based random unitary dynamics that can be costly to realize and sensitive to control imperfections, particularly on analog quantum hardware. We propose the chaotic quantum diffusion model, a framework that generates projected ensembles via chaotic Hamiltonian time evolution, providing a flexible and hardware-compatible diffusion mechanism. Requiring only global, time-independent control, our approach substantially reduces implementation overhead across diverse analog quantum platforms while achieving accuracy comparable to QuDDPMs. This method improves trainability and robustness, broadening the applicability of quantum generative modeling.
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