TY  - CONF
AU  - Delilbasic, Amer
AU  - Cavallaro, Gabriele
AU  - Willsch, Madita
AU  - Melgani, Farid
AU  - Riedel, Morris
AU  - Michielsen, Kristel
TI  - Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification
PB  - IEEE
M1  - FZJ-2021-02863
SP  - 2608-2611
PY  - 2021
AB  - 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.
T2  - IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
CY  - 12 Jul 2021 - 16 Jul 2021, Brussels (Belgium)
Y2  - 12 Jul 2021 - 16 Jul 2021
M2  - Brussels, Belgium
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
UR  - <Go to ISI:>//WOS:001250139802200
DO  - DOI:10.1109/IGARSS47720.2021.9554802
UR  - https://juser.fz-juelich.de/record/893824
ER  -