Journal Article

FZJ-2024-00269

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Kernel Approximation on a Quantum Annealer for Remote Sensing Regression Tasks

Pasetto, E.FZJ* ; Riedel, M.FZJ* ; Michielsen, K.FZJ* ; Cavallaro, G.FZJ*

2024
IEEE New York, NY

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Please use a persistent id in citations: doi:10.1109/JSTARS.2024.3350385  doi:10.34734/FZJ-2024-00269

Abstract: 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.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
3. EUROCC-2 (DEA02266) (DEA02266)
4. RAISE - Research on AI- and Simulation-Based Engineering at Exascale (951733) (951733)

Appears in the scientific report 2024

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Database coverage:
; ; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; DOAJ Seal ; IF >= 5 ; JCR ; SCOPUS ; Web of Science Core Collection

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