1. Nonnegative/binary matrix factorization with a D-Wave quantum annealer
- Author
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O'Malley, Daniel, Vesselinov, Velimir V., Alexandrov, Boian S., and Alexandrov, Ludmil B.
- Subjects
Computer Science - Machine Learning ,Quantum Physics ,Statistics - 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.
- Published
- 2017
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