TY  - JOUR
AU  - Pasetto, Edoardo
AU  - Riedel, Morris
AU  - Melgani, Farid
AU  - Michielsen, Kristel
AU  - Cavallaro, Gabriele
TI  - Quantum SVR for Chlorophyll Concentration Estimation in Water With Remote Sensing
JO  - IEEE geoscience and remote sensing letters
VL  - 19
SN  - 1545-598X
CY  - New York, NY
PB  - IEEE
M1  - FZJ-2022-03388
SP  - 1 - 5
PY  - 2022
AB  - 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 (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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000849255500003
DO  - DOI:10.1109/LGRS.2022.3200325
UR  - https://juser.fz-juelich.de/record/909757
ER  -