Contribution to a conference proceedings/Contribution to a book

FZJ-2022-03387

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing

Pasetto, E. (Corresponding author)FZJ* ; Delilbasic, A.FZJ* ; Cavallaro, G.FZJ* ; Willsch, M.FZJ* ; Melgani, F. ; Riedel, M.FZJ* ; Michielsen, K.FZJ*

2022
IEEE
ISBN: 978-1-6654-2792-0

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Please use a persistent id in citations: http://hdl.handle.net/2128/32041  doi:10.1109/IGARSS46834.2022.9883963

Abstract: 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.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2022

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Database coverage:

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