Bayesian Inference based on Quantum Computing

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the application of quantum computing to accelerate Bayesian inference. Traditional Bayesian inference suffers from significant computational bottlenecks, particularly in complex models with large datasets. We propose a novel approach leveraging the inherent parallelism of quantum computers to drastically reduce the time required for inference. Specifically, we design a quantum algorithm based on a parameterized quantum circuit (PQC) to perform Bayesian updating. This approach utilizes quantum superposition and interference to perform computations on multiple possibilities simultaneously. The core claim is that this method can significantly speed up Bayesian inference. We detail the mathematical formulation of the algorithm, including the quantum circuit representation and the key operations involved. The potential of this approach to overcome the limitations of classical Bayesian inference is discussed, highlighting the transformative impact of quantum computing on probabilistic reasoning and data analysis. The efficiency gains are expected to be particularly pronounced for problems where the prior distributions and the likelihood functions are highly correlated, allowing for efficient quantum interference.

DOI: 10.5281/zenodo.22274291

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