results: 研究结果显示，混合量子机器学习方法可以有效地处理大量的CT扫描图像，并且可以准确地分类COVID-19、CAP和正常三类。Abstract
Practical quantum computing (QC) is still in its infancy and problems considered are usually fairly small, especially in quantum machine learning when compared to its classical counterpart. Image processing applications in particular require models that are able to handle a large amount of features, and while classical approaches can easily tackle this, it is a major challenge and a cause for harsh restrictions in contemporary QC. In this paper, we apply a hybrid quantum machine learning approach to a practically relevant problem with real world-data. That is, we apply hybrid quantum transfer learning to an image processing task in the field of medical image processing. More specifically, we classify large CT-scans of the lung into COVID-19, CAP, or Normal. We discuss quantum image embedding as well as hybrid quantum machine learning and evaluate several approaches to quantum transfer learning with various quantum circuits and embedding techniques.
摘要
现代量子计算（QC）仍处于初期阶段，问题通常较小，尤其在量子机器学习领域，与经典机器学习相比。图像处理应用需要处理大量特征，而经典方法可以轻松实现，但在当代QC中是一个主要挑战，导致严格的限制。本文使用混合量子机器学习方法解决实际 relevance 的医学图像处理问题。具体来说，我们使用混合量子传输学习将大量 CT-扫描图像分类为COVID-19、CAP或正常。我们讨论量子图像嵌入以及混合量子机器学习，评估了各种量子循环和嵌入技术。