基于动态拓扑的量子机器学习框架

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning framework based on dynamic topology, leveraging the unique properties of quantum systems to perform complex machine learning tasks. We propose a method where the input to the quantum algorithm is dynamically constructed from a set of topological structures, and a dynamic topology optimization algorithm is employed to tailor the learning process. The core mechanism of this framework centers on the integration of dynamic topology into the quantum computation process, aiming to achieve efficient and effective quantum machine learning. The work presents a preliminary demonstration of this framework's capabilities through specific examples, highlighting its potential to overcome limitations of existing quantum machine learning approaches.

DOI: 10.5281/zenodo.22293451

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