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

Abstract

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; ; ; ;
Publication Date:
Research Org.:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1485190
Alternate Identifier(s):
OSTI ID: 1489946
Report Number(s):
LA-UR-17-22557
Journal ID: ISSN 1932-6203; 10.1371/journal.pone.0206653
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Published Article
Journal Name:
PLoS ONE
Additional Journal Information:
Journal Name: PLoS ONE Journal Volume: 13 Journal Issue: 12; Journal ID: ISSN 1932-6203
Publisher:
Public Library of Science (PLoS)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Computer Science; Mathematics

Citation Formats

O’Malley, Daniel, Vesselinov, Velimir V., Alexandrov, Boian S., Alexandrov, Ludmil B., and Olier, ed., Ivan. Nonnegative/Binary matrix factorization with a D-Wave quantum annealer. United States: N. p., 2018. Web. doi:10.1371/journal.pone.0206653.
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O’Malley, Daniel, Vesselinov, Velimir V., Alexandrov, Boian S., Alexandrov, Ludmil B., & Olier, ed., Ivan. Nonnegative/Binary matrix factorization with a D-Wave quantum annealer. United States. https://doi.org/10.1371/journal.pone.0206653
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O’Malley, Daniel, Vesselinov, Velimir V., Alexandrov, Boian S., Alexandrov, Ludmil B., and Olier, ed., Ivan. Mon . "Nonnegative/Binary matrix factorization with a D-Wave quantum annealer". United States. https://doi.org/10.1371/journal.pone.0206653.
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@article{osti_1485190,
title = {Nonnegative/Binary matrix factorization with a D-Wave quantum annealer},
author = {O’Malley, Daniel and Vesselinov, Velimir V. and Alexandrov, Boian S. and Alexandrov, Ludmil B. and Olier, ed., Ivan},
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 = {Mon Dec 10 00:00:00 EST 2018},
month = {Mon Dec 10 00:00:00 EST 2018}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1371/journal.pone.0206653
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Citation Metrics:
Cited by: 45 works
Citation information provided by
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Figures / Tables:

Fig. 1 Fig. 1: Face image reconstruction using features learned by NBMF. The five-by-seven matrix of images on the right shows the features that were learned. The two images on the left show the original image (top) and the reconstruction (bottom). The reconstruction is obtained by summing the features that are boxedmore » in green. Note that although some of the features appear to be all black, they actually contain facial features that are small in magnitude (black corresponds to 0, white corresponds to 1).« less
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Works referenced in this record:

Unsupervised feature selection by regularized self-representation
journal, February 2015

GPU Computing
journal, May 2008

Combining neighborhood separable subspaces for classification via sparsity regularized optimization
journal, November 2016

Non-convex regularized self-representation for unsupervised feature selection
journal, April 2017

The pitfalls of planar spin-glass benchmarks: raising the bar for quantum annealers (again)
journal, July 2017

  • Mandrà, Salvatore; Katzgraber, Helmut G.; Thomas, Creighton
  • Quantum Science and Technology, Vol. 2, Issue 3
  • DOI: 10.1088/2058-9565/aa7877

Face recognition using eigenfaces
conference, January 1991

  • Turk, M. A.; Pentland, A. P.
  • Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
  • DOI: 10.1109/CVPR.1991.139758

CPU DB: recording microprocessor history
journal, April 2012

  • Danowitz, Andrew; Kelley, Kyle; Mao, James
  • Communications of the ACM, Vol. 55, Issue 4
  • DOI: 10.1145/2133806.2133822

Quantum annealing with manufactured spins
journal, May 2011

  • Johnson, M. W.; Amin, M. H. S.; Gildert, S.
  • Nature, Vol. 473, Issue 7346
  • DOI: 10.1038/nature10012

Quantum adiabatic machine learning
journal, November 2012

Subspace clustering guided unsupervised feature selection
journal, June 2017

Tabu Search—Part I
journal, August 1989

Multi-label feature selection with missing labels
journal, February 2018

Optimised simulated annealing for Ising spin glasses
journal, July 2015

Projected Gradient Methods for Nonnegative Matrix Factorization
journal, October 2007

What is the Computational Value of Finite-Range Tunneling?
journal, August 2016

Minor-embedding in adiabatic quantum computation: II. Minor-universal graph design
journal, October 2010

Tabu Search—Part II
journal, February 1990

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

Quantum annealing in the transverse Ising model
journal, November 1998

Defining and detecting quantum speedup
journal, June 2014

Neuromorphic computing gets ready for the (really) big time
journal, June 2014

  • Monroe, Don
  • Communications of the ACM, Vol. 57, Issue 6
  • DOI: 10.1145/2601069

Partial Deconvolution With Inaccurate Blur Kernel
journal, January 2018

  • Ren, Dongwei; Zuo, Wangmeng; Zhang, David
  • IEEE Transactions on Image Processing, Vol. 27, Issue 1
  • DOI: 10.1109/TIP.2017.2764261

Dictionary Learning
journal, March 2011

From Point to Set: Extend the Learning of Distance Metrics
conference, December 2013

  • Zhu, Pengfei; Zhang, Lei; Zuo, Wangmeng
  • 2013 IEEE International Conference on Computer Vision (ICCV)
  • DOI: 10.1109/ICCV.2013.331

Adiabatic Quantum Computation and Quantum Annealing: Theory and Practice
journal, July 2014

Learning the parts of objects by non-negative matrix factorization
journal, October 1999

  • Lee, Daniel D.; Seung, H. Sebastian
  • Nature, Vol. 401, Issue 6755
  • DOI: 10.1038/44565

Chip makers turn to multicore processors
journal, January 2005

D-Wave upgrade: How scientists are using the world’s most controversial quantum computer
journal, January 2017

Fast clique minor generation in Chimera qubit connectivity graphs
journal, October 2015

  • Boothby, Tomas; King, Andrew D.; Roy, Aidan
  • Quantum Information Processing, Vol. 15, Issue 1
  • DOI: 10.1007/s11128-015-1150-6

Experimental evaluation of an adiabiatic quantum system for combinatorial optimization
conference, January 2013

  • McGeoch, Catherine C.; Wang, Cong
  • Proceedings of the ACM International Conference on Computing Frontiers - CF '13
  • DOI: 10.1145/2482767.2482797

Minor-embedding in adiabatic quantum computation: I. The parameter setting problem
journal, September 2008

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