Quantum Probability Neural Networks

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces Quantum Probability Neural Networks (QPNs), a novel deep learning architecture predicated on the unique properties of quantum mechanics. We explore the potential of leveraging quantum entanglement and superposition to enhance neural network expressiveness and learning efficiency. The core claim is that QPNs can outperform classical neural networks in certain probabilistic tasks, particularly those involving complex data patterns. This work investigates the fundamental principles behind QPNs and presents a preliminary model demonstrating the potential benefits of incorporating quantum elements into neural network design. We will address the challenges of quantum implementation and discuss future research directions.

DOI: 10.5281/zenodo.22291600

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