skip to main content

DOE PAGESDOE PAGES

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

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 whenmore » 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.« less
Authors:
ORCiD logo [1] ; ORCiD logo [2] ; ORCiD logo [2] ; ORCiD logo [3]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of Maryland Baltimore County (UMBC), Baltimore, MD (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of California, San Diego, CA (United States)
Publication Date:
Report Number(s):
LA-UR-17-22557
Journal ID: ISSN 1932-6203
Grant/Contract Number:
89233218CNA000001
Type:
Published Article
Journal Name:
PLoS ONE
Additional Journal Information:
Journal Volume: 13; Journal Issue: 12; Journal ID: ISSN 1932-6203
Publisher:
Public Library of Science
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE Laboratory Directed Research and Development (LDRD) Program
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING
OSTI Identifier:
1485190
Alternate Identifier(s):
OSTI ID: 1489946

O'Malley, Daniel, Vesselinov, Velimir Valentinov, Alexandrov, Boian S., and Alexandrov, Ludmil B.. Nonnegative/Binary matrix factorization with a D-Wave quantum annealer. United States: N. p., Web. doi:10.1371/journal.pone.0206653.
O'Malley, Daniel, Vesselinov, Velimir Valentinov, Alexandrov, Boian S., & Alexandrov, Ludmil B.. Nonnegative/Binary matrix factorization with a D-Wave quantum annealer. United States. doi:10.1371/journal.pone.0206653.
O'Malley, Daniel, Vesselinov, Velimir Valentinov, Alexandrov, Boian S., and Alexandrov, Ludmil B.. 2018. "Nonnegative/Binary matrix factorization with a D-Wave quantum annealer". United States. doi:10.1371/journal.pone.0206653.
@article{osti_1485190,
title = {Nonnegative/Binary matrix factorization with a D-Wave quantum annealer},
author = {O'Malley, Daniel and Vesselinov, Velimir Valentinov and Alexandrov, Boian S. and Alexandrov, Ludmil B.},
abstractNote = {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.},
doi = {10.1371/journal.pone.0206653},
journal = {PLoS ONE},
number = 12,
volume = 13,
place = {United States},
year = {2018},
month = {12}
}

Works referenced in this record:

Rapid object detection using a boosted cascade of simple features
conference, January 2001
  • Viola, P.; Jones, M.
  • Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001
  • DOI: 10.1109/CVPR.2001.990517