Quantum-Enhanced Constraint Propagation for Bayesian Inference

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper investigates the potential of quantum mechanics to accelerate and enhance Bayesian inference, specifically targeting complex probabilistic models. We propose a novel framework incorporating quantum algorithms to efficiently explore the parameter space, ultimately aiming to improve convergence rates and accuracy. The core challenge lies in adapting existing algorithms to the inherently complex nature of quantum systems, while maintaining the interpretability of the results. This work explores the application of quantum annealing and variational quantum eigensolver (VQE) techniques within a Bayesian context to address challenges inherent in probabilistic modeling. We present preliminary results demonstrating enhanced convergence compared to classical methods for a specific model, highlighting the potential for transformative advancements in Bayesian inference.

DOI: 10.5281/zenodo.22296156

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