自适应拓扑量子态的自适应几何建模

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel approach to quantum state modeling based on adaptive geometry. Traditional quantum state modeling often relies on static mappings, limiting their applicability to complex geometric structures. We propose a self-adaptive geometric modeling method that learns the dynamic evolution of the quantum state's geometry, enabling efficient modeling and simulation of complex topological structures. This approach leverages the inherent geometric properties of quantum states to dynamically adjust the representation, drastically improving computational efficiency. The core mechanism involves a learning algorithm that maps the quantum state's evolution to a geometric representation, allowing for iterative refinement and improved model accuracy. We demonstrate the effectiveness of this method through simulations of topological quantum circuits, showcasing enhanced computational speed and the ability to handle intricate geometric configurations.

DOI: 10.5281/zenodo.22495625

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