TY  - JOUR
AU  - Pasetto, Edoardo
AU  - Riedel, Morris
AU  - Michielsen, Kristel
AU  - Cavallaro, Gabriele
TI  - Kernel Approximation on a Quantum Annealer for Remote Sensing Regression Tasks
JO  - IEEE journal of selected topics in applied earth observations and remote sensing
VL  - 17
SN  - 1939-1404
CY  - New York, NY
PB  - IEEE
M1  - FZJ-2024-00269
SP  - 3262 - 3269
PY  - 2024
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001166899900001
DO  - DOI:10.1109/JSTARS.2024.3350385
UR  - https://juser.fz-juelich.de/record/1020574
ER  -