Conference Presentation (After Call)

FZJ-2020-04996

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-Wave Quantum Annealer

Cavallaro, G. (Corresponding author)FZJ* ; Willsch, D.FZJ* ; Willsch, M.FZJ* ; Michielsen, K.FZJ* ; Riedel, M.FZJ*

2020

Please use a persistent id in citations: http://hdl.handle.net/2128/26385

Abstract: Support Vector Machine (SVM) is a popular supervised Machine Learning (ML) method that is widely used for classification and regression problems. Recently, a method to train SVMs on a D-Wave 2000Q Quantum Annealer (QA) was proposed for binary classification of some biological data. First, ensembles of weak quantum SVMs are generated by training each classifier on a disjoint training subset that can be fit into the QA. Then, the computed weak solutions are fused for making predictions on unseen data. In this work, the classification of Remote Sensing (RS) multispectral images with SVMs trained on a QA is discussed. Furthermore, an open code repository is released to facilitate an early entry into the practical application of QA, a new disruptive compute technology.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 512 - Data-Intensive Science and Federated Computing (POF3-512) (POF3-512)
2. 511 - Computational Science and Mathematical Methods (POF3-511) (POF3-511)

Appears in the scientific report 2020

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Database coverage:

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