  Understanding how the D-Wave quantum computer could be used for machine
learning problems is of growing interest. Our work evaluates the feasibility of
using the D-Wave as a sampler for machine learning. We describe a hybrid system
that combines a classical deep neural network autoencoder with a quantum
annealing Restricted Boltzmann Machine (RBM) using the D-Wave. We evaluate our
hybrid autoencoder algorithm using two datasets, the MNIST dataset and MNIST
Fashion dataset. We evaluate the quality of this method by using a downstream
classification method where the training is based on quantum RBM-generated
samples. Our method overcomes two key limitations in the current 2000-qubit
D-Wave processor, namely the limited number of qubits available to accommodate
typical problem sizes for fully connected quantum objective functions and
samples that are binary pixel representations. As a consequence of these
limitations we are able to show how we achieved nearly a 22-fold compression
factor of grayscale 28 x 28 sized images to binary 6 x 6 sized images with a
lossy recovery of the original 28 x 28 grayscale images. We further show how
generating samples from the D-Wave after training the RBM, resulted in 28 x 28
images that were variations of the original input data distribution, as opposed
to recreating the training samples. We formulated an MNIST classification
problem using a deep convolutional neural network that used samples from a
quantum RBM to train the MNIST classifier and compared the results with an
MNIST classifier trained with the original MNIST training data set, as well as
an MNIST classifier trained using classical RBM samples. Our hybrid autoencoder
approach indicates advantage for RBM results relative to the use of a current
RBM classical computer implementation for image-based machine learning and even
more promising results for the next generation D-Wave quantum system.
