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.

我们描述了一种用于训练支持向量机的绝热量子方法，该方法的量子计算时间复杂度比经典方法好一个数量级，我们在五个基准数据集上比较了量子方法和经典方法的测试准确度，在可扩展性研究中，我们发现量子方法在具有许多特征的数据集上比经典方法快 3.5-4.5 倍。