Quantum Topology and Machine Learning: A Hybrid Learning Algorithm

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum topology, a theoretical framework describing the properties of space and its connection to quantum mechanics, has emerged as a promising area of research with potential applications in various fields, including quantum computing and machine learning. This paper explores the integration of quantum topology with machine learning, specifically focusing on the design of a novel self-adaptive learning algorithm leveraging the unique characteristics of quantum computation. The core idea is to utilize the inherent adaptability of quantum systems to optimize the learning process, thereby enhancing model performance and efficiency. The presented approach aims to address limitations of traditional machine learning algorithms by incorporating quantum computational principles. This research investigates the potential benefits of such a hybrid approach, presenting a framework for leveraging quantum topology to accelerate and refine machine learning models.

DOI: 10.5281/zenodo.22200528

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