Self-Adaptive Quantum Topology Modeling

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the potential of self-adaptive quantum topology modeling as a novel approach to quantum geometry. We introduce a framework predicated on the inherent properties of quantum topology, allowing for the learning of topological patterns and the generation of more sophisticated geometric models. The core mechanism involves leveraging these learned patterns to design models with enhanced capabilities. The research highlights the benefits of this approach over traditional methods, emphasizing the capacity for adaptation and improved precision in capturing complex quantum geometry.

DOI: 10.5281/zenodo.22183754

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