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000866292 1001_ $$0P:(DE-Juel1)167542$$aWillsch, D.$$b0$$eCorresponding author
000866292 245__ $$aSupport vector machines on the D-Wave quantum annealer
000866292 260__ $$aAmsterdam$$bNorth Holland Publ. Co.$$c2020
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000866292 520__ $$aKernel-based support vector machines (SVMs) are supervised machine learning algorithms for classification and regression problems. We introduce a method to train SVMs on a D-Wave 2000Q quantum annealer and study its performance in comparison to SVMs trained on conventional computers. The method is applied to both synthetic data and real data obtained from biology experiments. We find that the quantum annealer produces an ensemble of different solutions that often generalizes better to unseen data than the single global minimum of an SVM trained on a conventional computer, especially in cases where only limited training data is available. For cases with more training data than currently fits on the quantum annealer, we show that a combination of classifiers for subsets of the data almost always produces stronger joint classifiers than the conventional SVM for the same parameters.
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000866292 7001_ $$0P:(DE-Juel1)167543$$aWillsch, M.$$b1
000866292 7001_ $$0P:(DE-HGF)0$$aDe Raedt, Hans$$b2
000866292 7001_ $$0P:(DE-Juel1)138295$$aMichielsen, K.$$b3
000866292 773__ $$0PERI:(DE-600)1466511-6$$a10.1016/j.cpc.2019.107006$$gp. 107006 -$$p107006 -$$tComputer physics communications$$v248$$x0010-4655$$y2020
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