Title: Topological Information Theory Quantum Machine Learning Integration

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the integration of Topological Information Theory (TIT) with Quantum Machine Learning (QML) to achieve quantum acceleration and data compression in machine learning. Traditional machine learning relies on explicit parameter tuning, while this integration leverages the inherent structure of topological information theory for adaptive learning. We propose a novel method for utilizing the topological structure of quantum models as a source of noise, optimizing model parameters through topological analysis to improve efficiency and generalization. The core claim is that utilizing this approach will result in enhanced quantum acceleration and data compression compared to traditional machine learning. This work presents a framework for incorporating topological information into the quantum machine learning pipeline, demonstrating a potential paradigm shift in model optimization and efficiency.

DOI: 10.5281/zenodo.22202292

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