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.

Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing / Pasetto, E.; Delilbasic, A.; Cavallaro, G.; Willsch, M.; Melgani, F.; Riedel, M.; Michielsen, K.. - ELETTRONICO. - 2022:(2022), pp. 4903-4906. (Intervento presentato al convegno 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 tenutosi a Kuala Lumpur, Malaysia nel 17-22, July 2022) [10.1109/IGARSS46834.2022.9883963].

Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing

Melgani F.;
2022-01-01

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.
2022
International Geoscience and Remote Sensing Symposium (IGARSS)
New York, USA
Institute of Electrical and Electronics Engineers Inc.
978-1-6654-2792-0
Pasetto, E.; Delilbasic, A.; Cavallaro, G.; Willsch, M.; Melgani, F.; Riedel, M.; Michielsen, K.
Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing / Pasetto, E.; Delilbasic, A.; Cavallaro, G.; Willsch, M.; Melgani, F.; Riedel, M.; Michielsen, K.. - ELETTRONICO. - 2022:(2022), pp. 4903-4906. (Intervento presentato al convegno 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 tenutosi a Kuala Lumpur, Malaysia nel 17-22, July 2022) [10.1109/IGARSS46834.2022.9883963].
File in questo prodotto:
File Dimensione Formato  
IGARSS-Quantum.pdf

Solo gestori archivio

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 258.42 kB
Formato Adobe PDF
258.42 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/373010
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 0
social impact