Dynamic Quantum Topology Modeling

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel dynamic quantum topology modeling approach employing machine learning to automatically learn and model complex quantum topologies, and to enable dynamic optimization. Traditional topology modeling relies on manual parameter definition, which is computationally intensive. Our method leverages machine learning to autonomously model topology and then utilizes machine learning to dynamically optimize the topology, improving efficiency. The core mechanism involves learning a representation of the topology through machine learning, followed by applying optimization techniques to refine the topology. We demonstrate the effectiveness of this approach in modeling specific quantum topological structures and provide a framework for applying it to other topology problems. The resulting models offer a significant advancement over existing approaches, particularly in terms of automation and efficiency.

DOI: 10.5281/zenodo.22301855

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