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

doi:10.1109/IGARSS46834.2022.9883963

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

Faculty of Industrial Engineering, Mechanical Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4903-4906
Number of pages	4
ISBN (Electronic)	9781665427920
DOIs	https://doi.org/10.1109/IGARSS46834.2022.9883963
Publication status	Published - 17 Jul 2022
Event	2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia Duration: 17 Jul 2022 → 22 Jul 2022

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2022-July

Conference

Conference	2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Country/Territory	Malaysia
City	Kuala Lumpur
Period	17/07/22 → 22/07/22

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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10.1109/IGARSS46834.2022.9883963

Cite this

Pasetto, E., Delilbasic, A., Cavallaro, G., Willsch, M., Melgani, F., Riedel, M., & Michielsen, K. (2022). Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 4903-4906). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS46834.2022.9883963

Pasetto, Edoardo ; Delilbasic, Amer ; Cavallaro, Gabriele et al. / Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 4903-4906 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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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.",

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Pasetto, E, Delilbasic, A, Cavallaro, G, Willsch, M, Melgani, F, Riedel, M & Michielsen, K 2022, Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing. in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2022-July, Institute of Electrical and Electronics Engineers Inc., pp. 4903-4906, 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022, Kuala Lumpur, Malaysia, 17/07/22. https://doi.org/10.1109/IGARSS46834.2022.9883963

Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing. / Pasetto, Edoardo; Delilbasic, Amer; Cavallaro, Gabriele et al.

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc., 2022. p. 4903-4906 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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. Publisher Copyright: © 2022 IEEE.

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N2 - 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.

AB - 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.

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BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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Pasetto E, Delilbasic A, Cavallaro G, Willsch M, Melgani F, Riedel M et al. Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc. 2022. p. 4903-4906. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS46834.2022.9883963

Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing

Abstract

Publication series

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Bibliographical note

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