Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Regression analysis has a crucial role in many Earth Ob-servation (EO) applications. The increasing availability and recent development of new computing technologies moti-vate further research to expand the capabilities and enhance the performance of data analysis algorithms. In this paper, the biophysical variable estimation problem is addressed. A novel approach is proposed, which consists in a reformulated Support Vector Regression (SVR) and leverages Quantum Annealing (QA). In particular, the SVR optimization prob-lem is reframed to a Quadratic Unconstrained Binary Opti-mization (QUBO) problem. The algorithm is then tested on the D-Wave Advantage quantum annealer. The experiments presented in this paper show good results, despite current hardware limitations, suggesting that this approach is viable and has great potential.

Original languageEnglish
Title of host publicationIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4903-4906
Number of pages4
ISBN (Electronic)9781665427920
DOIs
Publication statusPublished - 17 Jul 2022
Event2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia
Duration: 17 Jul 202222 Jul 2022

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2022-July

Conference

Conference2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Country/TerritoryMalaysia
CityKuala Lumpur
Period17/07/2222/07/22

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.

Publisher Copyright:
© 2022 IEEE.

Other keywords

  • quantum annealing
  • quantum computing
  • quantum machine learning
  • remote sensing
  • Support vector regression

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