2018/01/30 by Maxwell Henderson, John Novak, Tristan Cook · 2 voices
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Quantum Computing Algorithms and Architecture #Quantum many-body systems #cs.ET #cs.LG #quant-ph
paper · pdf · doi:10.7566/jpsj.88.061009
openalex created_date 2018/02/23 · openalex publication_date 2019/03/01 · openalex updated_date 2026/07/28
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.