Quantum Machine Learning investigates the possibility of quantum computers
enhancing Machine Learning algorithms. Anomaly segmentation is a fundamental
task in various domains to identify irregularities at sample level and can be
addressed with both supervised and unsupervised methods. Autoencoders are
commonly used in unsupervised tasks, where models are trained to reconstruct
normal instances efficiently, allowing anomaly identification through high
reconstruction errors. While quantum autoencoders have been proposed in the
literature, their application to anomaly segmentation tasks remains unexplored.
In this paper, we introduce a patch-based quantum autoencoder (QPB-AE) for
image anomaly segmentation, with a number of parameters scaling logarithmically
with patch size. QPB-AE reconstructs the quantum state of the embedded input
patches, computing an anomaly map directly from measurement through a SWAP test
without reconstructing the input image. We evaluate its performance across
multiple datasets and parameter configurations and compare it against a
classical counterpart.

这篇论文介绍了一种基于量子自编码器的图像异常分割方法，其中参数的数量与图像块的大小对数缩放。该方法通过量子状态的重构，通过 SWAP 测试对嵌入输入块计算异常映射，而无需重建输入图像，并在多个数据集和参数配置下评估其性能并与经典对应方法进行比较。