Use of a Quantum Computer and the Quick Medical Reference To Give an Approximate Diagnosis

TL;DR

This paper explores how quantum computers can be used to perform approximate inference in Bayesian networks, specifically for medical diagnosis, potentially offering computational advantages over classical methods.

Contribution

It introduces quantum algorithms for rejection sampling and likelihood weighting in Bayesian networks, extending their application to quantum computing for medical diagnosis.

Findings

Quantum algorithms for Bayesian inference are feasible.

Potential for faster inference with quantum computing.

Applicability to other Bayesian networks.

Abstract

The Quick Medical Reference (QMR) is a compendium of statistical knowledge connecting diseases to findings (symptoms). The information in QMR can be represented as a Bayesian network. The inference problem (or, in more medical language, giving a diagnosis) for the QMR is to, given some findings, find the probability of each disease. Rejection sampling and likelihood weighted sampling (a.k.a. likelihood weighting) are two simple algorithms for making approximate inferences from an arbitrary Bayesian net (and from the QMR Bayesian net in particular). Heretofore, the samples for these two algorithms have been obtained with a conventional "classical computer". In this paper, we will show that two analogous algorithms exist for the QMR Bayesian net, where the samples are obtained with a quantum computer. We expect that these two algorithms, implemented on a quantum computer, can also be used to make inferences (and predictions) with other Bayesian nets.