2008/06/24 by Robert R. Tucci, Tucci, Robert R. · 1 citation
Computer Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Physical sciences #Quantum Mechanics and Applications #Quantum Physics (quant-ph) #cs.AI #quant-ph
paper · pdf · doi:10.48550/arxiv.0806.3949
v1:14 pages (files: 1 .tex, 1 .sty, 5 .eps);v2:19 pages (files: 1 .tex, 1 .sty, 5 .eps)added stuff about likelihood weighting
openalex publication_date 2008/06/24 · arxiv created 2008/07/29 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.