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Activity Number: 621 - Quantum Computing: Algorithms and Applications
Type: Topic Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract #329176
Title: Leveraging Adiabatic Quantum Computation for Election Forecasting
Author(s): Maxwell Henderson*
Companies: QxBranch
Keywords: quantum; quantum computing; machine learning; neural networks; forecasting; models

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.

Authors who are presenting talks have a * after their name.

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