TY - CONF
T1 - QUANTUM SUPPORT VECTOR MACHINE ALGORITHMS FOR REMOTE SENSING DATA CLASSIFICATION
AU - Delilbasic, Amer
AU - Cavallaro, Gabriele
AU - Willsch, Madita
AU - Melgani, Farid
AU - Riedel, Morris
AU - Michielsen, Kristel
N1 - Funding Information: The authors gratefully acknowledge the Jülich Supercomputing Centre for funding this project by providing computing time on the D-Wave Advantage system through the Jülich UNified Infrastructure for Quantum computing (JUNIQ). M.W. acknowledges support from the project JUNIQ that has received funding from the German Federal Ministry of Education and Research (BMBF) and the Ministry of Culture and Science of the State of North Rhine-Westphalia. Part of this work was performed in the CoE RAISE project receiving funding from the European Union’s Horizon 2020 Research and Innovation Framework Programme H2020-INFRAEDI-2019-1 under grant agreement no. 951733. Publisher Copyright: © 2021 IEEE
PY - 2021
Y1 - 2021
N2 - 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.
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.
KW - classification
KW - quantum annealing
KW - quantum circuit model
KW - quantum computing
KW - quantum machine learning
KW - remote sensing
KW - support vector machine
UR - https://www.scopus.com/pages/publications/85128549787
U2 - 10.1109/IGARSS47720.2021.9554802
DO - 10.1109/IGARSS47720.2021.9554802
M3 - Paper
SP - 2608
EP - 2611
T2 - 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Y2 - 12 July 2021 through 16 July 2021
ER -