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.
|Title of host publication
|IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 17 Jul 2022
|2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia
Duration: 17 Jul 2022 → 22 Jul 2022
|International Geoscience and Remote Sensing Symposium (IGARSS)
|2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
|17/07/22 → 22/07/22
Bibliographical noteFunding 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.
© 2022 IEEE.
- quantum annealing
- quantum computing
- quantum machine learning
- remote sensing
- Support vector regression