# Nonnegative/binary matrix factorization with a D-Wave quantum annealer

## TL;DR

This paper demonstrates how the D-Wave quantum annealer can be integrated into an unsupervised machine learning framework to analyze large datasets, specifically for feature extraction from facial images.

## Contribution

It introduces a novel application of D-Wave quantum annealing for nonnegative/binary matrix factorization in machine learning tasks.

## Key findings

- Effective use of D-Wave 2X for unsupervised learning
- Ability to analyze large datasets with quantum annealing
- Successful feature extraction from facial images

## 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.