  Adiabatic quantum computers can solve difficult optimization problems (e.g.,
the quadratic unconstrained binary optimization problem), and they seem well
suited to train machine learning models. In this paper, we describe an
adiabatic quantum approach for training support vector machines. We show that
the time complexity of our quantum approach is an order of magnitude better
than the classical approach. Next, we compare the test accuracy of our quantum
approach against a classical approach that uses the Scikit-learn library in
Python across five benchmark datasets (Iris, Wisconsin Breast Cancer (WBC),
Wine, Digits, and Lambeq). We show that our quantum approach obtains accuracies
on par with the classical approach. Finally, we perform a scalability study in
which we compute the total training times of the quantum approach and the
classical approach with increasing number of features and number of data points
in the training dataset. Our scalability results show that the quantum approach
obtains a 3.5--4.5 times speedup over the classical approach on datasets with
many (millions of) features.
