Use of a Quantum Computer to do Importance and Metropolis-Hastings Sampling of a Classical Bayesian Network

TL;DR

This paper proposes methods to perform importance sampling and Metropolis-Hastings sampling of classical Bayesian networks using a quantum computer, potentially enhancing sampling efficiency.

Contribution

It introduces novel quantum algorithms for classical Bayesian network sampling, bridging quantum computing with probabilistic graphical models.

Findings

Proposed quantum algorithms for importance sampling.

Proposed quantum algorithms for Metropolis-Hastings sampling.

Potential for improved sampling efficiency on quantum hardware.

Abstract

Importance sampling and Metropolis-Hastings sampling (of which Gibbs sampling is a special case) are two methods commonly used to sample multi-variate probability distributions (that is, Bayesian networks). Heretofore, the sampling of Bayesian networks has been done on a conventional "classical computer". In this paper, we propose methods for doing importance sampling and Metropolis-Hastings sampling of a classical Bayesian network on a quantum computer.