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| 001 | 909064 | ||
| 005 | 20220822191449.0 | ||
| 024 | 7 | _ | |a 10.36227/techrxiv.19619676.v1 |2 doi |
| 024 | 7 | _ | |a 2128/31648 |2 Handle |
| 037 | _ | _ | |a FZJ-2022-02982 |
| 100 | 1 | _ | |a Pasetto, Edoardo |0 P:(DE-Juel1)191143 |b 0 |u fzj |
| 245 | _ | _ | |a Quantum SVR for Chlorophyll Concentration Estimation in Water with Remote Sensing |
| 260 | _ | _ | |c 2022 |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1661157777_5165 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a The increasing availability of quantum computers motivates researching their potential capabilities in enhancing the performance of data analysis algorithms. Similarly as in other research communities, also in Remote Sensing (RS) it is not yet defined how its applications can benefit from the usage of quantum computing. This paper proposes a formulation of the Support Vector Regression (SVR) algorithm that can be executed by D-Wave quantum computers. Specifically, the SVR is mapped to a Quadratic Unconstrained Binary Optimization (QUBO) optimization problem that is solved with Quantum Annealing (QA). The algorithm is tested on two different types of computing environments offered by D-Wave: The Advantage system, which directly embeds the problem into the Quantum Processing Unit (QPU), and a Hybrid solver that employs both classical and quantum computing resources. For the evaluation, we considered a biophysical variable estimation problem with RS data. The experimental results show that the proposed quantum SVR implementation can achieve comparable or in some cases better results than the classical implementation. This work is one of the first attempts to provide insight into how QA could be exploited and integrated in future RS workflows based on Machine Learning algorithms. |
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| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Riedel, Morris |0 P:(DE-Juel1)132239 |b 1 |
| 700 | 1 | _ | |a Melgani, Farid |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Michielsen, Kristel |0 P:(DE-Juel1)138295 |b 3 |u fzj |
| 700 | 1 | _ | |a Cavallaro, Gabriele |0 P:(DE-Juel1)171343 |b 4 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.36227/techrxiv.19619676.v1 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/909064/files/Edoardo_Pasetto_Manuscript_April_20_2022.pdf |y OpenAccess |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 0 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 1 |
| 914 | 1 | _ | |y 2022 |
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| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
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| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
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