Application of Quantum Annealing to Training of Deep Neural Networks

Understand

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.