Fast training of fully-connected Boltzmann Machines on an Adiabatic Quantum Computer

We demonstrate how to leverage thermal noise within an Adiabatic Quantum Computer (AQC) to train a complete Boltzmann Machine (BM). In contrast to previous implementations, our fully-connected BM includes hidden units and we sample both negative and positive statistics from the AQC. By training the network on a standard benchmark Bars and Stripes dataset, the maximally parallelized classical algorithm takes 11.4 times longer to reach the same likelihood as the quantum algorithm, indicating that AQCs may expedite BM training. The ability of AQCs to sample Boltzmann-distributed configurations quickly has the potential to impact BMs training as well as influence the field of stochastic machine learning.