Quantum Patch-Based Autoencoder for Anomaly Segmentation

Quantum Machine Learning investigates the pos-sibility 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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2024 IEEE International Conference on Quantum Computing and Engineering (QCE) Jg. 1; S. 259 - 267
Hauptverfasser: Madeira, Maria Francisca, Poggiali, Alessandro, Lorenz, Jeanette Miriam
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 15.09.2024
Schlagworte:
Anomaly Segmen-tation
Autoencoders
Computational modeling
Data models
Image reconstruction
Image segmentation
Location awareness
Noise
Quantum Autoencoder
Quantum computing
Quantum Machine Learning
Scalability
Training
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Quantum Machine Learning investigates the pos-sibility 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, al-lowing 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.
DOI:10.1109/QCE60285.2024.00039