Quantum Topology for Machine Learning Algorithm Generation

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum topology, a relatively recent field of physics, has emerged as a promising area for advancements in computation and information processing. Its unique properties, such as the existence of topological phases and their robustness, have attracted significant attention across diverse disciplines. This paper explores the potential of quantum topology to revolutionize machine learning by introducing a novel algorithm that automatically generates new machine learning models. We propose a framework centered around the manipulation of quantum topological states to achieve efficient and adaptable model creation. The core mechanism involves harnessing the inherent properties of quantum topology to dynamically adjust model parameters, thereby optimizing learning efficiency. This paper details the design of the algorithm, its potential benefits, and preliminary results demonstrating its effectiveness.

DOI: 10.5281/zenodo.22205825

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