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

• Published in: PloS one Vol. 13; no. 12; p. e0206653
• Main Authors: O'Malley, Daniel, Vesselinov, Velimir V, Alexandrov, Boian S, Alexandrov, Ludmil B
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.12.2018; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Annealing; Artificial intelligence; Benchmarking; Benchmarks; Biology and Life Sciences; Computer and Information Sciences; Computer applications; Computer Science; Computing time; Datasets; Decomposition; Earth science; Engineering and Technology; Information processing; Integer programming; Laboratories; Learning algorithms; Machine Learning; Mathematics; MATHEMATICS AND COMPUTING; Medicine and Health Sciences; Models, Theoretical; Novels; Pattern recognition; Physical Sciences; Problem solving; Problems; Quantum Theory; Research and Analysis Methods; Social Sciences; Teaching methods; Iran; New Mexico; United States > US; California
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0206653

D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest. Much of this interest has focused on the quantum behavior of D-Wave machines, and there have been few practical algorithms that use the D-Wave. 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 takes a matrix as input and produces two low-rank matrices as output-one containing latent features in the data and another matrix describing how the features can be combined to approximately reproduce the input matrix. Despite the limited number of bits in the D-Wave hardware, this method is capable of handling a large input matrix. The D-Wave only limits the rank of the two output matrices. We apply this method to learn the features from a set of facial images and compare the performance of the D-Wave to two classical tools. This method is able to learn facial features and accurately reproduce the set of facial images. The performance of the D-Wave shows some promise, but has some limitations. It outperforms the two classical codes in a benchmark when only a short amount of computational time is allowed (200-20,000 microseconds), but these results suggest heuristics that would likely outperform the D-Wave in this benchmark.