Quantum Topology Machine Learning

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Topology Machine Learning (QTML) is a novel approach to machine learning that leverages the unique properties of quantum topology to design and optimize algorithms. This research explores the potential of quantum computation to enhance machine learning performance, specifically focusing on the development of more efficient and accurate models. The core claim of this work is that QTML offers a fundamentally different approach to machine learning, capitalizing on the quantum properties of spacetime to achieve superior results. This paper investigates the theoretical foundations and practical applications of QTML, emphasizing the creation of novel quantum algorithms and the exploration of potential improvements to existing machine learning techniques. We introduce a framework for identifying and exploiting quantum topological features to enhance model training and prediction. The investigation will consider the impact of quantum entanglement and superposition on the learning process.

DOI: 10.5281/zenodo.22299591

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