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| 001 | 909756 | ||
| 005 | 20230502100726.0 | ||
| 020 | _ | _ | |a 978-1-6654-2792-0 |
| 024 | 7 | _ | |a 10.1109/IGARSS46834.2022.9883963 |2 doi |
| 024 | 7 | _ | |a 2128/32041 |2 Handle |
| 024 | 7 | _ | |a WOS:000920916604255 |2 WOS |
| 037 | _ | _ | |a FZJ-2022-03387 |
| 100 | 1 | _ | |a Pasetto, Edoardo |0 P:(DE-Juel1)191143 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a IEEE International Geoscience and Remote Sensing Symposium (IGARSS) |g IGARSS 2022 |c Kuala Lumpur |d 2022-07-17 - 2022-07-22 |w Malaysia |
| 245 | _ | _ | |a Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing |
| 260 | _ | _ | |c 2022 |b IEEE |
| 300 | _ | _ | |a 4903-4906 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
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| 520 | _ | _ | |a 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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| 700 | 1 | _ | |a Michielsen, Kristel |0 P:(DE-Juel1)138295 |b 6 |
| 773 | _ | _ | |a 10.1109/IGARSS46834.2022.9883963 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/909756/files/IGARSS_2022_Quantum_SVR.pdf |y OpenAccess |
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