Nonnegative/Binary Matrix Factorization for Image Classification using
  Quantum Annealing
Hinako Asaoka Kazue Kudo
Classical computing has borne witness to the development of machine learning.
The integration of quantum technology into this mix will lead to unimaginable
benefits and be regarded as a giant leap forward in mankind's ability to
compute. Demonstrating the benefits of this integration now becomes essential.
With the advance of quantum computing, several machine-learning techniques have
been proposed that use quantum annealing. In this study, we implement a matrix
factorization method using quantum annealing for image classification and
compare the performance with traditional machine-learning methods.
Nonnegative/binary matrix factorization (NBMF) was originally introduced as a
generative model, and we propose a multiclass classification model as an
application. We extract the features of handwritten digit images using NBMF and
apply them to solve the classification problem. Our findings show that when the
amount of data, features, and epochs is small, the accuracy of models trained
by NBMF is superior to classical machine-learning methods, such as neural
networks. Moreover, we found that training models using a quantum annealing
solver significantly reduces computation time. Under certain conditions, there
is a benefit to using quantum annealing technology with machine learning.