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. T

SUBMITTER: O'Malley D 

PROVIDER: S-EPMC6287781 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature