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

Abstract · Apr 5, 2017 18:49 ·

cs-lg quant-ph stat-ml

Arxiv Abstract

  • Daniel O'Malley
  • Velimir V. Vesselinov
  • Boian S. Alexandrov
  • Ludmil B. Alexandrov

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.

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