Quantum Bayesian Inference: An Exploration

Jon Frost, Carlos Madeira, Yash Rastogi and Harald Uhlig ()
Additional contact information
Yash Rastogi: Georgia Institute of Technology
Harald Uhlig: University of Chicago

No 2026-40, Working Papers from Becker Friedman Institute for Research In Economics

Abstract: This paper introduces a framework for performing Bayesian inference using quantum computation. It presents a proof-of-concept quantum algorithm that performs posterior sampling. We provide an accessible introduction to quantum computation for economists and a practical demonstration of quantum-based posterior sampling for Bayesian estimation. Our key contribution is the preparation of a quantum state whose measurement yields samples from a discretized posterior distribution. While the proposed approach does not yet offer computational speedups over classical techniques such as Markov Chain Monte Carlo, it highlights both the conceptual promise and practical challenges in integrating quantum computation into the econometrician’s toolbox.

Keywords: Quantum computing; Bayesian estimator; Bayesian inference; Markov chain Monte Carlo (MCMC) algorithms; Gibbs sampling (search for similar items in EconPapers)
JEL-codes: C11 C20 C30 C50 C60 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2026
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