基于量子拓扑的自适应机器学习模型生成

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum Topology, a novel paradigm of spacetime geometry, offers a promising framework for computation and information processing. Its inherent properties, such as topological defects and wormholes, provide a unique set of mathematical and physical characteristics that can be leveraged to design fundamentally different computational models. This paper explores the application of quantum topology to machine learning, focusing on the development of a self-adaptive learning model generator. We introduce a method that utilizes quantum state dynamics to produce increasingly diverse machine learning models, enhancing their generalization capabilities. The core mechanism involves manipulating the quantum state to induce changes in the model's representation, thereby promoting robustness and adaptability. This research delves into the potential of this approach to overcome limitations of traditional machine learning techniques, particularly in areas requiring complex and unpredictable outputs.

DOI: 10.5281/zenodo.22157721

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