  Kernel-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.
