Support vector machines on the D-Wave quantum annealer
This work addresses improving SVM generalization for classification and regression problems, particularly in data-scarce scenarios, but is incremental as it adapts existing methods to quantum hardware.
The authors tackled training support vector machines (SVMs) using a D-Wave quantum annealer, finding that it produces an ensemble of solutions that often generalize better to unseen data than conventional SVMs, especially with limited training data, and combining classifiers for subsets of data yields stronger joint classifiers in data-rich cases.
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.