Szczegóły publikacji
Opis bibliograficzny
Hyperspectral image segmentation with a machine learning model trained using quantum annealer / Dawid Mazur, Tomasz RYBOTYCKI, Piotr GAWRON // W: Computational Science – ICCS 2025 Workshops : 25th international conference : Singapore, Singapore, July 7–9, 2025 : proceedings, Pt. 5 / eds. Maciej Paszyński, Amanda S. Barnard, Yongjie Jessica Zhang. — Cham : Springer Nature Switzerland, cop. 2025. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 15911). — ISBN: 978-3-031-97569-1; e-ISBN: 978-3-031-97570-7. — S. 195-209. — Bibliogr., Abstr. — Publikacja dostępna online od: 2025-07-05. — T. Rybotycki, P. Gawron - dod. afiliacja: Polish Academy of Sciences, Warsaw
Autorzy (3)
Dane bibliometryczne
| ID BaDAP | 161066 |
|---|---|
| Data dodania do BaDAP | 2025-07-18 |
| DOI | 10.1007/978-3-031-97570-7_16 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2025 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Training of machine learning models consumes large amounts of energy. Since the energy consumption becomes a major problem in the development and implementation of artificial intelligence systems there exists a need to investigate the ways to reduce use of the resources by these systems. In this work we study how application of quantum annealers could lead to reduction of energy cost in training models aiming at pixel-level segmentation of hyperspectral images. Following the results of QBM4EO team, we propose a classical machine learning model, partially trained using quantum annealer, for hyperspectral image segmentation. We show that the model trained using quantum annealer is better or at least comparable with models trained using alternative algorithms, according to the preselected, common metrics. While direct energy use comparison does not make sense at the current stage of quantum computing technology development, we prove that quantum annealing should be considered as a tool for training at least some machine learning models.
