  In Deep Learning, a well-known approach for training a Deep Neural Network
starts by training a generative Deep Belief Network model, typically using
Contrastive Divergence (CD), then fine-tuning the weights using backpropagation
or other discriminative techniques. However, the generative training can be
time-consuming due to the slow mixing of Gibbs sampling. We investigated an
alternative approach that estimates model expectations of Restricted Boltzmann
Machines using samples from a D-Wave quantum annealing machine. We tested this
method on a coarse-grained version of the MNIST data set. In our tests we found
that the quantum sampling-based training approach achieves comparable or better
accuracy with significantly fewer iterations of generative training than
conventional CD-based training. Further investigation is needed to determine
whether similar improvements can be achieved for other data sets, and to what
extent these improvements can be attributed to quantum effects.
