基于量子纠缠的自适应几何映射

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper investigates the potential of utilizing quantum entanglement to enable self-adaptive geometric mapping. Traditional geometric mapping methods rely on predefined mapping models, limiting design flexibility. We propose a novel system leveraging quantum entanglement to learn mapping parameters from data, facilitating the creation of complex geometric relationships. The core mechanism centers around establishing entangled states to represent geometric transformations, allowing for a dynamic and adaptable mapping process. This approach offers a fundamentally new pathway for geometric modeling, potentially surpassing the limitations of conventional techniques. The paper details the system's architecture, the quantum entanglement process, and the resulting geometric mappings demonstrated through illustrative examples. We highlight the advantages of this approach in terms of adaptability, efficiency, and the potential for generating intricate geometric structures.

DOI: 10.5281/zenodo.22318930

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