  Accurate, reliable sampling from fully-connected graphs with arbitrary
correlations is a difficult problem. Such sampling requires knowledge of the
probabilities of observing every possible state of a graph. As graph size
grows, the number of model states becomes intractably large and efficient
computation requires full sampling be replaced with heuristics and algorithms
that are only approximations of full sampling. This work investigates the
potential impact of adiabatic quantum computation for sampling purposes,
building on recent successes training Boltzmann machines using a quantum
device. We investigate the use case of quantum computation to train Boltzmann
machines for predicting the 2016 Presidential election.
