量子去噪扩散模型

摘要

近年来，诸如DALL-E、Craiyon和Stable Diffusion等机器学习模型因其能够从简洁描述中生成高分辨率图像的能力而受到广泛关注。与此同时，量子计算显示出有希望的进展，特别是在量子机器学习方面，它利用量子力学来满足传统机器学习算法日益增长的计算需求。本文探讨了量子机器学习和变分量子电路的整合，以增强基于扩散的图像生成模型的效果。具体而言，我们解决了经典扩散模型的两个挑战：低采样速度和庞大的参数需求。我们引入了两个量子扩散模型，并使用MNIST数字、时尚MNIST和CIFAR-10对它们的能力进行了基准测试，与其经典对应物相比。我们的模型在性能指标FID、SSIM和PSNR方面超越了具有相似参数数量的经典模型。此外，我们引入了一种一致性模型——幺正单采样架构，将扩散过程合并为一步，实现快速的一步图像生成。

English

In recent years, machine learning models like DALL-E, Craiyon, and Stable Diffusion have gained significant attention for their ability to generate high-resolution images from concise descriptions. Concurrently, quantum computing is showing promising advances, especially with quantum machine learning which capitalizes on quantum mechanics to meet the increasing computational requirements of traditional machine learning algorithms. This paper explores the integration of quantum machine learning and variational quantum circuits to augment the efficacy of diffusion-based image generation models. Specifically, we address two challenges of classical diffusion models: their low sampling speed and the extensive parameter requirements. We introduce two quantum diffusion models and benchmark their capabilities against their classical counterparts using MNIST digits, Fashion MNIST, and CIFAR-10. Our models surpass the classical models with similar parameter counts in terms of performance metrics FID, SSIM, and PSNR. Moreover, we introduce a consistency model unitary single sampling architecture that combines the diffusion procedure into a single step, enabling a fast one-step image generation.

量子去噪扩散模型

Quantum Denoising Diffusion Models

摘要

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