论文标题
物理图像中的量子增强群集检测
Quantum-enhanced cluster detection in physical images
论文作者
论文摘要
在许多领域中识别数据中的簇是一项重要任务。在本文中，我们考虑了数据生活在物理世界中的情况，因此我们必须先使用传感器收集图像，然后再将其聚类。使用量子纠缠增强的传感器，我们可以比使用纯粹的经典策略更准确地对表面进行映像。但是，如果我们获得的优势足够强大，可以在诸如聚类之类的数据处理步骤中幸存下来，这一点并不明显。以前已经发现，使用量子增强的传感器进行成像和模式识别可以为监督学习任务带来优势，在这里我们证明，这种优势也适用于无监督的学习任务，即聚类。
Identifying clusters in data is an important task in many fields. In this paper, we consider situations in which data live in a physical world, so we have to first collect the images using sensors before clustering them. Using sensors enhanced by quantum entanglement, we can image surfaces more accurately than using purely classical strategies. However, it is not immediately obvious if the advantage we gain is robust enough to survive data processing steps such as clustering. It has previously been found that using quantum-enhanced sensors for imaging and pattern recognition can give an advantage for supervised learning tasks, and here we demonstrate that this advantage also holds for an unsupervised learning task, namely clustering.
