Adiabatic Quantum Support Vector Machines
Abstract
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.
Keywords
Cite
@article{arxiv.2401.12485,
title = {Adiabatic Quantum Support Vector Machines},
author = {Prasanna Date and Dong Jun Woun and Kathleen Hamilton and Eduardo A. Coello Perez and Mayanka Chandra Shekhar and Francisco Rios and John Gounley and In-Saeng Suh and Travis Humble and Georgia Tourassi},
journal= {arXiv preprint arXiv:2401.12485},
year = {2024}
}