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...

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Bibliographic Details

Published in:	2024 IEEE International Conference on Quantum Computing and Engineering (QCE) Vol. 1; pp. 259 - 267
Main Authors:	Madeira, Maria Francisca, Poggiali, Alessandro, Lorenz, Jeanette Miriam
Format:	Conference Proceeding
Language:	English
Published:	IEEE 15.09.2024
Subjects:	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 Access:	Get full text
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