发表机构
Universitat Pompeu Fabra; Barcelona Supercomputing Center; University of Florence; LENS - European Laboratory for Non-Linear Spectroscopy; ICREA（庞培法布拉大学; 巴塞罗那超级计算中心; 佛罗伦萨大学; LENS——欧洲非线性光谱实验室; 加泰罗尼亚研究与高级研究机构）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种可扩展的混合量子扩散模型，利用离散时间量子游走算法在真实量子设备上处理医学图像，并验证其在灰度、RGB及3D数据上的生成能力优于经典对应模型。
AI 中文摘要
量子机器学习是一个新颖的研究领域，旨在开发利用量子力学原理（如叠加、纠缠和干涉）的机器学习方法。在此背景下，我们提出了一种可扩展的混合量子扩散模型，并评估其在医学图像分析中的应用。具体而言，我们的方法基于离散时间量子游走算法，在真实量子设备上执行，以模拟扩散模型的前向动力学。对于扩散模型的后向步骤，我们设计并评估了一个经典学习模型，用于反向去噪数据。与现有其他尝试将量子机器学习应用于图像分析任务的方法相比，这些方法受限于现有量子设备的规模，我们的方法能够处理真实世界的大规模医学数据。特别是，我们展示了灰度图像、RGB图像以及中等规模3D体积的结果。我们通过复现一个基于离散状态空间扩散模型的经典对应模型来基准测试我们的结果。通过这样做，我们在图像生成领域的三个不同最新指标上比较了两种模型的生成能力，展示了我们方法的竞争性和有前景的结果。
英文摘要
Quantum Machine Learning is a novel field of research aimed at devising machine learning approaches exploiting principles of quantum mechanics, such as superposition, entanglement and interference. In this context, we present a scalable hybrid Quantum Diffusion Model, and evaluate its use for medical image analysis. Specifically, our method is based on a Discrete-Time Quantum Walk algorithm, executed on a real quantum device, to model the forward dynamics of the diffusion model. For the backward step of the diffusion model, we devise and evaluate a classical learning model, which is used to reversely denoise the data. In contrast with other existing attempts at applying quantum machine learning for image analysis tasks, severely limited by the size of existing quantum devices, our method allows to process real-world large size medical data. In particular, we present results on grayscale and RGB images, as well as 3D volumes of moderate sizes. We benchmark our results by reproducing an alternative classical counterpart model, based on diffusion models on discrete state spaces. By doing so, we compare the generation capabilities of both models in terms of three distinct state-of-the-art metrics in the field of image generation, showing the competitive, promising results of our approach.
Comments12 pages, 12 supplementary pages, 7 figures, 1 table, 12 supplementary figures
