  Restricted Boltzmann Machine (RBM) is an energy based, undirected graphical
model. It is commonly used for unsupervised and supervised machine learning.
Typically, RBM is trained using contrastive divergence (CD). However, training
with CD is slow and does not estimate exact gradient of log-likelihood cost
function. In this work, the model expectation of gradient learning for RBM has
been calculated using a quantum annealer (D-Wave 2000Q), which is much faster
than Markov chain Monte Carlo (MCMC) used in CD. Training and classification
results are compared with CD. The classification accuracy results indicate
similar performance of both methods. Image reconstruction as well as
log-likelihood calculations are used to compare the performance of quantum and
classical algorithms for RBM training. It is shown that the samples obtained
from quantum annealer can be used to train a RBM on a 64-bit `bars and stripes'
data set with classification performance similar to a RBM trained with CD.
Though training based on CD showed improved learning performance, training
using a quantum annealer eliminates computationally expensive MCMC steps of CD.
