Dynamic Quantum Topology Learning Algorithm: Tuning Parameters for Enhanced System Modeling

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper introduces a dynamic quantum topology learning algorithm designed to optimize system modeling through adaptive parameter adjustments. Traditional learning algorithms often rely on fixed parameters, limiting their ability to capture complex system dynamics. Our approach dynamically adjusts quantum circuit parameters based on input data and system state, fostering a more robust and adaptable system representation. We propose a novel mechanism leveraging quantum circuit simulation to establish a direct connection between system model and topology, enabling fine-tuning and improved performance. The algorithm's core is a continuous parameter adjustment process guided by system observations.

DOI: 10.5281/zenodo.22224699

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