Nonnegative/binary matrix factorization with a D-Wave quantum annealer
Machine Learning
2019-03-06 v1 Quantum Physics
Machine Learning
Abstract
D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest, but have been used for few real-world computations. Machine learning has been identified as an area where quantum annealing may be useful. Here, we show that the D-Wave 2X can be effectively used as part of an unsupervised machine learning method. This method can be used to analyze large datasets. The D-Wave only limits the number of features that can be extracted from the dataset. We apply this method to learn the features from a set of facial images.
Cite
@article{arxiv.1704.01605,
title = {Nonnegative/binary matrix factorization with a D-Wave quantum annealer},
author = {Daniel O'Malley and Velimir V. Vesselinov and Boian S. Alexandrov and Ludmil B. Alexandrov},
journal= {arXiv preprint arXiv:1704.01605},
year = {2019}
}