Template-Type: ReDIF-Paper 1.0
Author-Name: Jon Frost
Author-X-Name-First: Jon
Author-X-Name-Last: Frost
Author-Name: Carlos Madeira
Author-X-Name-First: Carlos
Author-X-Name-Last: Madeira
Author-Name: Yash Rastogi
Author-X-Name-First: Yash
Author-X-Name-Last: Rastogi
Author-Name: Harald Uhlig
Author-X-Name-First: Harald
Author-X-Name-Last: Uhlig
Title: Quantum Bayesian inference: an exploration
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 discretised posterior distribution. While the proposed approach does not yet offer computational speedups over classical techniques such as Markov Chain Monte Carlo, it demonstrates the feasibility of simulating Bayesian inference with quantum computation. This work serves as a first step in integrating quantum computation into the econometrician's toolbox. It highlights both the conceptual promise and practical challenges – especially those related to quantum state preparation – in leveraging quantum computation for Bayesian inference.
Creation-Date: 2026-04
File-URL: https://www.bis.org/publ/work1342.pdf
File-Format: Application/pdf
File-Function: Full PDF document
File-URL: https://www.bis.org/publ/work1342.htm
File-Format: text/html
Number: 1342
Keywords: quantum computing; Bayesian estimator; Bayesian inference; Markov chain Monte Carlo (MCMC) algorithms; Gibbs sampling
Classification-JEL: C11, C20, C30, C50, C60
Handle: RePEc:bis:biswps:1342