%0 Conference Paper
%A Pasetto, Edoardo
%A Delilbasic, Amer
%A Cavallaro, Gabriele
%A Willsch, Madita
%A Melgani, Farid
%A Riedel, Morris
%A Michielsen, Kristel
%T Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing
%I IEEE
%M FZJ-2022-03387
%@  978-1-6654-2792-0
%P 4903-4906
%D 2022
%X Regression analysis has a crucial role in many Earth Observation (EO) applications. The increasing availability and recent development of new computing technologies motivate 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 problem is reframed to a Quadratic Unconstrained Binary Optimization (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.
%B IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
%C 17 Jul 2022 - 22 Jul 2022, Kuala Lumpur (Malaysia)
Y2 17 Jul 2022 - 22 Jul 2022
M2 Kuala Lumpur, Malaysia
%F PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
%9 Contribution to a conference proceedingsContribution to a book
%U <Go to ISI:>//WOS:000920916604255
%R 10.1109/IGARSS46834.2022.9883963
%U https://juser.fz-juelich.de/record/909756