Quantum Patch-Based Autoencoder for Anomaly Segmentation
This work addresses anomaly segmentation for image analysis, but it is incremental as it applies quantum autoencoders to a new task without major breakthroughs.
The paper tackles anomaly segmentation in images by introducing a patch-based quantum autoencoder (QPB-AE) that reconstructs quantum states of patches and computes anomaly maps via a SWAP test, achieving performance comparable to classical methods across multiple datasets.
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.