Nonnegative/binary matrix factorization with a D-Wave quantum annealer
This work addresses the challenge of leveraging quantum annealing for real-world machine learning tasks, specifically in image analysis, though it is incremental as it builds on existing factorization methods with a new computational tool.
The authors tackled the problem of using a D-Wave quantum annealer for unsupervised machine learning by applying it to nonnegative/binary matrix factorization, achieving effective feature extraction from facial images with the limitation being the number of features the D-Wave can handle.
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.