  In this work we revisit and expand on a 2015 study that used a D-Wave quantum
annealer as a sampling engine to assist in the training of a Deep Neural
Network. The original 2015 results were reproduced using more recent D-Wave
hardware. We systematically compare this quantum-assisted training method to a
wider range of classical techniques, including: Contrastive Divergence with a
different choice of optimizer; Contrastive Divergence with an increased number
of steps (CD-k); and Simulated Annealing (SA). We find that quantum-assisted
training still outperforms the CD with Gibbs sampling-based techniques;
however, SA is able to match the performance of quantum-assisted training
trivially using a quench-like schedule with a single sweep at high temperature
followed by one at the target temperature.

Quantum Physics