Adaptive Topological Geometry for Machine Learning

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces an adaptive quantum topological geometry framework for machine learning, aiming to enhance model precision and efficiency through the exploitation of quantum topological geometry. Traditional machine learning relies heavily on classical algorithms, limiting scalability and potentially introducing suboptimal solutions. This framework leverages the unique properties of quantum entanglement to enable self-adaptive learning, resulting in improved model accuracy and efficiency. The core mechanism involves quantum-induced topological transformations that guide the learning process, optimizing the model's representation and minimizing error. This novel approach offers a promising shift towards more robust and efficient machine learning algorithms.

DOI: 10.5281/zenodo.22187933

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