Quantum Topology Neural Network

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Topology Neural Network (QTNN) is a novel neural network architecture that leverages quantum entanglement and topological structures to enhance data modeling and learning capabilities. This paper explores the fundamental principles behind QTN and details its implementation, showcasing its potential for improved model performance compared to traditional neural networks. We present a comprehensive overview of the proposed architecture, including the mathematical foundations, key design choices, and experimental results demonstrating its efficacy. The core of QTN lies in its ability to create and manipulate quantum states through entanglement, enabling the network to explore complex data relationships in a fundamentally different way. This investigation aims to establish QTN as a promising approach for advancing machine learning, particularly within the context of data-intensive applications.

DOI: 10.5281/zenodo.22498139

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