利用真实量子硬件噪声生成图像，提升生成质量并探索量子噪声新用法。
Physics-inspired Generative AI models via real hardware-based noisy quantum diffusion
- 结合量子随机行走与经典动态，设计更鲁棒的生成流程。
- 在四量子比特硬件上实现图像生成，MNIST图像FID更低。
- 不消除噪声，反而利用真实量子设备噪声作为生成资源。
量子扩散模型（QDMs）是生成式AI中一种新兴范式，旨在利用量子特性提升经典模型性能。然而，现有算法因近端量子设备限制难以扩展。基于前期工作，本文提出并实现了两种受物理启发的协议。第一种采用量子随机行走形式，通过前向过程中量子与经典动力学的特定协同，生成的MNIST图像具有比纯经典动力学更低的弗雷切特初始距离（FID）。第二种方法利用真实IBM量子硬件的内在噪声，在仅四量子比特条件下实现图像生成。本工作为大规模量子生成式AI开辟新路径，使量子噪声不再被抑制或纠正，而是作为有用资源加以利用。
原文摘要 · Abstract (English)
Quantum Diffusion Models (QDMs) are an emerging paradigm in Generative AI that aims to use quantum properties to improve the performances of their classical counterparts. However, existing algorithms are not easily scalable due to the limitations of near-term quantum devices. Following our previous work on QDMs, here we propose and implement two physics-inspired protocols. In the first, we use the formalism of quantum stochastic walks, showing that a specific interplay of quantum and classical dynamics in the forward process produces statistically more robust models generating sets of MNIST images with lower Fréchet Inception Distance (FID) than using totally classical dynamics. In the second approach, we realize an algorithm to generate images by exploiting the intrinsic noise of real IBM quantum hardware with only four qubits. Our work could be a starting point to pave the way for new scenarios for large-scale algorithms in quantum Generative AI, where quantum noise is neither mitigated nor corrected, but instead exploited as a useful resource.
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