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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Vydané v:	2024 IEEE International Conference on Quantum Computing and Engineering (QCE) Ročník 1; s. 259 - 267
Hlavní autori:	Madeira, Maria Francisca, Poggiali, Alessandro, Lorenz, Jeanette Miriam
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 15.09.2024
Predmet:	Anomaly Segmen-tation Autoencoders Computational modeling Data models Image reconstruction Image segmentation Location awareness Noise Quantum Autoencoder Quantum computing Quantum Machine Learning Scalability Training
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