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.

翻译：绝热量子计算机能够解决困难的优化问题（例如二次无约束二元优化问题），且似乎非常适合训练机器学习模型。本文提出了一种基于绝热量子方法训练支持向量机的方案。我们证明，该量子方法的时间复杂度比经典方法低一个数量级。接下来，我们在五个基准数据集（Iris、威斯康星乳腺癌(WBC)、Wine、Digits和Lambeq）上，将量子方法的测试准确率与使用Python中Scikit-learn库的经典方法进行比较。结果表明，量子方法获得的准确率与经典方法相当。最后，我们进行了一项可扩展性研究，计算了在训练数据集中特征数量和样本数量增加时，量子方法与经典方法的总训练时间。可扩展性结果显示，在包含数百万级特征的数据集上，量子方法相比经典方法获得了3.5-4.5倍的加速。