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.

Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification / Delilbasic, Amer; Cavallaro, Gabriele; Willsch, Madita; Melgani, Farid; Riedel, Morris; Michielsen, Kristel. - (2021), pp. 2608-2611. (Intervento presentato al convegno IGARSS 2021 tenutosi a Brussels, Belgium nel 12-16, July, 2021) [10.1109/IGARSS47720.2021.9554802].

Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification

Melgani, Farid;
2021-01-01

Abstract

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.
2021
Proceedings of IEEE International Geoscience and Remote Sensing Symposium IGARSS 2021
New York, USA
IEEE
978-1-6654-0369-6
Delilbasic, Amer; Cavallaro, Gabriele; Willsch, Madita; Melgani, Farid; Riedel, Morris; Michielsen, Kristel
Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification / Delilbasic, Amer; Cavallaro, Gabriele; Willsch, Madita; Melgani, Farid; Riedel, Morris; Michielsen, Kristel. - (2021), pp. 2608-2611. (Intervento presentato al convegno IGARSS 2021 tenutosi a Brussels, Belgium nel 12-16, July, 2021) [10.1109/IGARSS47720.2021.9554802].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/329679
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