Bayesian Inference Based on Quantum Neural Networks

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper proposes a novel approach to Bayesian inference leveraging the capabilities of Quantum Neural Networks (QNNs). Traditional Bayesian inference methods suffer from significant computational complexity, particularly when dealing with large datasets and intricate models. This research addresses this limitation by formulating Bayesian inference as a training problem for a QNN. The core idea involves harnessing the inherent parallelism of QNNs to perform efficient probability calculations, thereby accelerating the inference process. We explore the theoretical framework and outline the key components of this model, emphasizing the synergy between quantum machine learning and Bayesian inference principles. The presented approach offers a potentially transformative pathway towards scalable and efficient Bayesian reasoning, particularly in domains where classical methods are struggling to keep pace with increasing data volumes and model complexity. This work lays the groundwork for future research and development in this burgeoning field.

DOI: 10.5281/zenodo.22138959

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