Assessing the Feasibility of Quantum Autoencoders for Real-World Visual Anomaly Detection
Year: 2026
Type: conference-paper
Abstract: Quantum Machine Learning (QML) has recently emerged as a promising paradigm for extending classical learning models by leveraging quantum-mechanical properties such as superposition and entanglement. Among the proposed approaches, quantum autoencoders have been introduced as a potential tool for data compression and anomaly detection. However, their applicability to real-world, high-dimensional computer vision tasks remains largely unexplored.In this work, we assess the feasibility of quantum autoencoders for visual anomaly detection using a realistic road-infrastructure dataset characterized by strong class imbalance and fine-grained spatial annotations. We propose a hybrid quantum–classical architecture that combines a classical feature extractor with a variational quantum autoencoder operating on a reduced latent space. The model is designed to process local image patches and detect anomalies based on reconstruction error.We benchmark the proposed approach against a classical autoencoder baseline under the same preprocessing and training conditions. The evaluation considers both reconstruction performance and anomaly detection capability, with particular attention to the challenges posed by sparse and localized anomalies in real-world imagery. The results provide insight into the current capabilities and limitations of quantum autoencoders in practical computer vision settings, highlighting key challenges related to data encoding, model scalability, and integration with classical pipelines.
Institutions: National Institute for Astrophysics, University of Messina
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7171980687