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000909757 1001_ $$0P:(DE-Juel1)191143$$aPasetto, Edoardo$$b0$$eCorresponding author
000909757 245__ $$aQuantum SVR for Chlorophyll Concentration Estimation in Water With Remote Sensing
000909757 260__ $$aNew York, NY$$bIEEE$$c2022
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000909757 520__ $$aThe increasing availability of quantum computers motivates researching their potential capabilities in enhancing the performance of data analysis algorithms. Similarly, as in other research communities, also in remote sensing (RS), it is not yet defined how its applications can benefit from the usage of quantum computing (QC). This letter proposes a formulation of the support vector regression (SVR) algorithm that can be executed by D-Wave quantum computers. Specifically, the SVR is mapped to a quadratic unconstrained binary optimization (QUBO) problem that is solved with quantum annealing (QA). The algorithm is tested on two different types of computing environments offered by D-Wave: the advantage system, which directly embeds the problem into the quantum processing unit (QPU), and a hybrid solver that employs both classical and QC resources. For the evaluation, we considered a biophysical variable estimation problem with RS data. The experimental results show that the proposed quantum SVR implementation can achieve comparable or, in some cases, better results than the classical implementation. This work is one of the first attempts to provide insight into how QA could be exploited and integrated in future RS workflows based on machine learning (ML) algorithms.
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000909757 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b1$$ufzj
000909757 7001_ $$00000-0001-9745-3732$$aMelgani, Farid$$b2
000909757 7001_ $$0P:(DE-Juel1)138295$$aMichielsen, Kristel$$b3$$ufzj
000909757 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b4
000909757 773__ $$0PERI:(DE-600)2138738-2$$a10.1109/LGRS.2022.3200325$$gVol. 19, p. 1 - 5$$p1 - 5$$tIEEE geoscience and remote sensing letters$$v19$$x1545-598X$$y2022
000909757 8564_ $$uhttps://juser.fz-juelich.de/record/909757/files/Invoice_APC600345220.pdf
000909757 8564_ $$uhttps://juser.fz-juelich.de/record/909757/files/Edoardo_Pasetto_GRSL_2022.pdf$$yOpenAccess
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