Title: Quantum Bayesian Neural Networks for Ensemble Learning

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum Bayesian Neural Networks (QBNNs) represent a novel approach to neural network architecture, integrating quantum superposition and entanglement to enhance ensemble learning performance, particularly when dealing with high-dimensional data. This paper explores the potential of QBNNs to overcome the limitations of classical ensembles by leveraging quantum phenomena for improved representation, processing, and generalization. We detail the design of a QBNN framework, focusing on the interplay between quantum states, Bayesian inference, and ensemble aggregation. The core claim centers on the design of a QBNN that utilizes quantum superposition and entanglement to achieve significantly enhanced accuracy and robustness in ensemble learning scenarios. The paper presents a comprehensive analysis of the theoretical and practical implications of this approach, including mathematical formulations and experimental validation demonstrating improvements over traditional ensemble methods. We discuss the challenges and future directions for this research, emphasizing the potential for transformative advancements in data analysis and machine learning.

DOI: 10.5281/zenodo.22277890

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