Dynamic Topological Information Learning

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the application of dynamic quantum state learning to the design of quantum devices and the optimization of quantum computation. We propose a novel algorithm that leverages the inherent dynamism of quantum states to learn complex topological structures. This learning process allows for a more flexible and adaptable approach compared to traditional methods, which often rely on pre-defined models. The core mechanism involves dynamically evolving quantum states to capture the underlying topology, and this capture is then used to optimize quantum device performance. The resulting design of quantum devices offers improved performance and adaptability, and this work explores the potential of this approach in various quantum computing applications. The study demonstrates the effectiveness of this dynamic learning strategy through a series of simulations and analysis.

DOI: 10.5281/zenodo.22187778

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