Topological-Quantum Fusion Learning (TFL)

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces Topological-Quantum Fusion Learning (TFL), a novel approach to quantum system modeling that integrates topology learning and quantum learning. TFL leverages dynamic adjustments to the quantum potential field to achieve more precise modeling of quantum systems and improved prediction accuracy. The core mechanism of TFL centers around the synergistic combination of topology-learning and quantum-learning, employing a feedback loop that iteratively refines the quantum system's state. The paper discusses the advantages of this hybrid methodology and presents preliminary results demonstrating its efficacy in modeling complex quantum systems.

DOI: 10.5281/zenodo.22486871

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