Contribution to a conference proceedings

FZJ-2024-05101

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Quantum Annealing for Semantic Segmentation in Remote Sensing: Potential and Limitations

Delilbasic, A.FZJ* ; Le Saux, B. ; Riedel, M.FZJ* ; Michielsen, K.FZJ* ; Cavallaro, G.FZJ*

2024
IEEE

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Please use a persistent id in citations: doi:10.1109/M2GARSS57310.2024.10537465  doi:10.34734/FZJ-2024-05101

Abstract: Quantum Annealing (QA) is a powerful method for combinatorial optimisation derived from adiabatic quantum computation. The development of computing devices implementing QA accelerated its adoption in practical use cases. In this paper, we summarise the main features and limitations of QA and its application to remote sensing, specifically to semantic segmentation. We provide indications for successfully applying it to real problems, and techniques for improving its performance. This overview can support practitioners in the adoption of this innovative computing technology.

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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. RAISE - Research on AI- and Simulation-Based Engineering at Exascale (951733) (951733)
3. AIDAS - Joint Virtual Laboratory for AI, Data Analytics and Scalable Simulation (aidas_20200731) (aidas_20200731)
4. EUROCC-2 (DEA02266) (DEA02266)

Appears in the scientific report 2024

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Database coverage:

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