# Support vector machines on the D-Wave quantum annealer

## TL;DR

This paper explores training support vector machines using a D-Wave quantum annealer, comparing its performance to traditional methods on synthetic and biological data, highlighting potential advantages in generalization and ensemble approaches.

## Contribution

It introduces a novel method for training SVMs on a quantum annealer and demonstrates its benefits over conventional training, especially with limited data.

## Key findings

- Quantum annealer produces diverse solutions that generalize better.
- Ensemble classifiers from subsets outperform standard SVMs.
- Quantum approach is effective with limited training data.

## Abstract

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.