%0 Journal Article
%A Pasetto, Edoardo
%A Riedel, Morris
%A Michielsen, Kristel
%A Cavallaro, Gabriele
%T Kernel Approximation on a Quantum Annealer for Remote Sensing Regression Tasks
%J IEEE journal of selected topics in applied earth observations and remote sensing
%V 17
%@ 1939-1404
%C New York, NY
%I IEEE
%M FZJ-2024-00269
%P 3262 - 3269
%D 2024
%X The increased development of quantum computing hardware in recent years has led to increased interest in its application to various areas. Finding effective ways to apply this technology to real-world use-cases is a current area of research in the (RS) community. This paper proposes an (AQKS) kernel approximation algorithm with parallel quantum annealing on the D-Wave Advantage quantum annealer. The proposed implementation is applied to (SVR) and (GPR) algorithms. To evaluate its performance, a regression problem related to estimating chlorophyll concentration in water is considered. The proposed algorithm was tested on two real-world datasets and its results were compared with those obtained by a classical implementation of kernel-based algorithms and a (RKS) implementation. On average, the parallel (AQKS) achieved comparable results to the benchmark methods, indicating its potential for future applications.
%F PUB:(DE-HGF)16
%9 Journal Article
%U <Go to ISI:>//WOS:001166899900001
%R 10.1109/JSTARS.2024.3350385
%U https://juser.fz-juelich.de/record/1020574