Title: Quantum-Accelerated Bayesian Inference - Novel Algorithm for Uncertainty Quantification

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel algorithm for Bayesian inference, leveraging quantum computation to significantly accelerate the process of uncertainty quantification. Traditional Bayesian methods often struggle with computationally intensive tasks, particularly when dealing with complex models and large datasets. We introduce a quantum-enhanced inference framework that employs Grover's search to efficiently identify optimal parameter configurations, thereby improving the accuracy and speed of uncertainty quantification. The algorithm's core mechanism involves quantum-assisted search, offering a substantial improvement over existing techniques in this domain. We detail the algorithm's architecture, discuss its performance characteristics, and provide a comprehensive analysis of its potential impact on uncertainty quantification workflows. The paper concludes with a discussion of future research directions and the implications of this quantum-accelerated approach.

DOI: 10.5281/zenodo.22187221

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