QUANTUM SUPPORT VECTOR MACHINE ALGORITHMS FOR REMOTE SENSING DATA CLASSIFICATION

Amer Delilbasic; Gabriele Cavallaro; Madita Willsch; Farid Melgani; Morris Riedel; Kristel Michielsen

doi:10.1109/IGARSS47720.2021.9554802

QUANTUM SUPPORT VECTOR MACHINE ALGORITHMS FOR REMOTE SENSING DATA CLASSIFICATION

Amer Delilbasic
, Gabriele Cavallaro
, Madita Willsch
, Farid Melgani
, Morris Riedel
, Kristel Michielsen

Faculty of Industrial Engineering, Mechanical Engineering and Computer Science

University of Iceland

Research output: Contribution to conference › Paper › peer-review

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.

Original language	English
Pages	2608-2611
Number of pages	4
DOIs	https://doi.org/10.1109/IGARSS47720.2021.9554802
Publication status	Published - 2021
Event	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium Duration: 12 Jul 2021 → 16 Jul 2021

Conference

Conference	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/Territory	Belgium
City	Brussels
Period	12/07/21 → 16/07/21

Bibliographical note

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

Other keywords

classification
quantum annealing
quantum circuit model
quantum computing
quantum machine learning
remote sensing
support vector machine

Access to Document

10.1109/IGARSS47720.2021.9554802

Cite this

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title = "QUANTUM SUPPORT VECTOR MACHINE ALGORITHMS FOR REMOTE SENSING DATA CLASSIFICATION",

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.",

keywords = "classification, quantum annealing, quantum circuit model, quantum computing, quantum machine learning, remote sensing, support vector machine",

author = "Amer Delilbasic and Gabriele Cavallaro and Madita Willsch and Farid Melgani and Morris Riedel and Kristel Michielsen",

note = "Funding Information: The authors gratefully acknowledge the J{\"u}lich Supercomputing Centre for funding this project by providing computing time on the D-Wave Advantage system through the J{\"u}lich 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{\textquoteright}s Horizon 2020 Research and Innovation Framework Programme H2020-INFRAEDI-2019-1 under grant agreement no. 951733. Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 ; Conference date: 12-07-2021 Through 16-07-2021",

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doi = "10.1109/IGARSS47720.2021.9554802",

language = "English",

pages = "2608--2611",

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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

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DO - 10.1109/IGARSS47720.2021.9554802

M3 - Paper

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EP - 2611

T2 - 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

Y2 - 12 July 2021 through 16 July 2021

ER -

QUANTUM SUPPORT VECTOR MACHINE ALGORITHMS FOR REMOTE SENSING DATA CLASSIFICATION

Abstract

Conference

Bibliographical note

Other keywords

Access to Document

Fingerprint

Cite this