Dynamic Topology Geometry: A Quantum Machine Learning Framework

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper investigates the potential of dynamic topology geometry as a foundation for a novel quantum machine learning framework, specifically designed for learning and applying topological structures. We propose a system leveraging quantum machine learning algorithms to generate and optimize complex topological structures, offering enhanced adaptability and improved optimization capabilities compared to traditional machine learning approaches. The core mechanism centers around integrating quantum mechanics with topology, allowing for the exploration of intricate and highly structured data. The framework aims to dynamically construct and manipulate topological landscapes, enabling the learning of complex relationships and ultimately, improved performance in applications requiring topological understanding. This research explores the implications of this approach and its potential impact on various fields, including data analysis, pattern recognition, and optimization problems.

DOI: 10.5281/zenodo.22289559

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