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| Hauptseite > Publikationsdatenbank > Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification > print |
| Hauptseite > Publikationsdatenbank > Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification > print |
| 001 | 893824 | ||
| 005 | 20250401102814.0 | ||
| 024 | 7 | _ | |a 10.1109/IGARSS47720.2021.9554802 |2 doi |
| 024 | 7 | _ | |a 2128/31336 |2 Handle |
| 024 | 7 | _ | |a WOS:001250139802200 |2 WOS |
| 037 | _ | _ | |a FZJ-2021-02863 |
| 100 | 1 | _ | |a Delilbasic, Amer |0 P:(DE-HGF)0 |b 0 |
| 111 | 2 | _ | |a IEEE International Geoscience and Remote Sensing Symposium (IGARSS) |c Brussels |d 2021-07-12 - 2021-07-16 |w Belgium |
| 245 | _ | _ | |a Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification |
| 260 | _ | _ | |c 2021 |b IEEE |
| 295 | 1 | 0 | |a 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS : [Proceedings] - IEEE, 2021. - ISBN 978-1-6654-0369-6 - doi:10.1109/IGARSS47720.2021.9554802 |
| 300 | _ | _ | |a 2608-2611 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
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| 520 | _ | _ | |a Recent developments in Quantum Computing (QC) have paved the way for an enhancement of computing capabilities. Quantum Machine Learning (QML) aims at developing Machine Learning (ML) models specifically designed for quantum computers. The availability of the first quantum processors enabled further research, in particular the exploration of possible practical applications of QML algorithms. In this work, quantum formulations of the Support Vector Machine (SVM) are presented. Then, their implementation using existing quantum technologies is discussed and Remote Sensing (RS) image classification is considered for evaluation. |
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| 700 | 1 | _ | |a Cavallaro, Gabriele |0 P:(DE-Juel1)171343 |b 1 |e Corresponding author |
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| 773 | _ | _ | |a 10.1109/IGARSS47720.2021.9554802 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/893824/files/IGARSS_2021_Delilbasic.pdf |y OpenAccess |
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