Contribution to a conference proceedings/Contribution to a book

FZJ-2021-02863

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification

Delilbasic, A. ; Cavallaro, G. (Corresponding author)FZJ* ; Willsch, M.FZJ* ; Melgani, F. ; Riedel, M.FZJ* ; Michielsen, K.FZJ*

2021
IEEE

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Please use a persistent id in citations: http://hdl.handle.net/2128/31336  doi:10.1109/IGARSS47720.2021.9554802

Abstract: Recent developments in Quantum Computing (QC) have paved the way for an enhancement of computing capabilities. Quantum Machine Learning (QML) aims at developing Machine Learning (ML) models specifically designed for quantum computers. The availability of the first quantum processors enabled further research, in particular the exploration of possible practical applications of QML algorithms. In this work, quantum formulations of the Support Vector Machine (SVM) are presented. Then, their implementation using existing quantum technologies is discussed and Remote Sensing (RS) image classification is considered for evaluation.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
3. AIDAS - Joint Virtual Laboratory for AI, Data Analytics and Scalable Simulation (aidas_20200731) (aidas_20200731)
4. AISee - AI- and Simulation-Based Engineering at Exascale (951733) (951733)

Appears in the scientific report 2021

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Database coverage:

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