自适应拓扑结构的量子纠缠学习算法

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper presents a novel quantum entanglement learning algorithm leveraging the principles of adaptive topology. Traditional quantum entanglement learning methods often rely on manipulating individual qubits, while this approach aims to dynamically construct and refine a quantum state to optimize entanglement properties. We introduce a framework centered around the construction of a 'topology map' that guides the evolution of the quantum state, allowing for a more efficient and accurate learning process. The core mechanism focuses on exploiting the inherent properties of quantum topology to represent and manipulate entanglement, leading to improved entanglement fidelity and reduced computational complexity. The algorithm demonstrates promising results in achieving precise entanglement manipulation, offering a potential pathway to enhanced quantum information processing.

DOI: 10.5281/zenodo.22221966

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