标题： 基于真实硬件噪声量子扩散的物理学启发生成式人工智能模型 显示英文标题

标题： Physics-inspired Generative AI models via real hardware-based noisy quantum diffusion

摘要： 量子扩散模型（QDMs）是生成式人工智能中的一个新兴范式，旨在利用量子特性来提升其经典对应物的性能。然而，由于近期量子设备的限制，现有的算法难以扩展。在我们之前关于QDMs的工作基础上，这里我们提出了两种基于物理学启发的协议并加以实现。在第一种方法中，我们使用量子随机游走的形式主义，展示了前向过程中量子和经典动力学之间的特定相互作用可以产生统计上更稳健的模型，生成具有更低Fréchet Inception距离（FID）的MNIST图像集，优于完全经典的动态过程。在第二种方法中，我们设计了一种算法，通过利用真实IBM量子硬件（仅有四个量子比特）的固有噪声来生成图像。我们的工作可能是为量子生成式人工智能的大规模算法开辟新场景的一个起点，在这种情况下，量子噪声既未被缓解也未被校正，而是被当作一种有用的资源加以利用。 显示英文摘要

摘要： 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 Fr\'echet 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.