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000893824 1001_ $$0P:(DE-HGF)0$$aDelilbasic, Amer$$b0
000893824 1112_ $$aIEEE International Geoscience and Remote Sensing Symposium (IGARSS)$$cBrussels$$d2021-07-12 - 2021-07-16$$wBelgium
000893824 245__ $$aQuantum Support Vector Machine Algorithms for Remote Sensing Data Classification
000893824 260__ $$bIEEE$$c2021
000893824 29510 $$a2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS : [Proceedings] - IEEE, 2021. - ISBN 978-1-6654-0369-6 - doi:10.1109/IGARSS47720.2021.9554802
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000893824 520__ $$aRecent 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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000893824 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b1$$eCorresponding author
000893824 7001_ $$0P:(DE-Juel1)167543$$aWillsch, Madita$$b2$$ufzj
000893824 7001_ $$0P:(DE-HGF)0$$aMelgani, Farid$$b3
000893824 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b4
000893824 7001_ $$0P:(DE-Juel1)138295$$aMichielsen, Kristel$$b5
000893824 773__ $$a10.1109/IGARSS47720.2021.9554802
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