Leveraging Adiabatic Quantum Computation for Election Forecasting

TL;DR

This paper explores the potential of adiabatic quantum computation to improve sampling in complex probabilistic models, specifically for training Boltzmann machines to forecast election outcomes.

Contribution

It introduces the application of adiabatic quantum computing to enhance sampling efficiency in probabilistic models for election forecasting.

Findings

Quantum-assisted training of Boltzmann machines shows promise.

Potential for improved sampling efficiency in large graphs.

Application to real-world election prediction scenarios.

Abstract

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.