# Leveraging Adiabatic Quantum Computation for Election Forecasting

arXiv: https://arxiv.org/abs/1802.00069 · HTML: https://mlsanity.com/paper/1802.00069/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2018.

## Built on by (most cited first, 1 of 1)

- [Assessment of image generation by quantum annealer](https://mlsanity.com/paper/2103.08373/) (2021)

## Abstract (arXiv)

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.