Title: Geometric Topology for Algorithmic Diversity in Quantum Machine Learning

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum machine learning holds immense potential for revolutionizing various fields, but its inherent complexity necessitates innovative approaches to algorithm design. Geometric topology, a field that studies the properties of spaces defined by continuous functions, offers a promising avenue for addressing this challenge. This paper proposes a novel geometric topology framework for quantum machine learning, aiming to generate diverse and robust algorithms by systematically exploring different geometric structures representing the data and the machine learning algorithm. We outline a method for generating and manipulating geometric shapes, emphasizing their role as fundamental building blocks for diverse and stable quantum machine learning models. The core mechanism centers on leveraging the inherent properties of geometric shapes to create a dynamic landscape of possibilities, thereby promoting algorithmic diversity. This research explores the potential of using geometric topology to overcome limitations of current quantum machine learning techniques and advance the field.

DOI: 10.5281/zenodo.22278657

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