基于图的非线性几何变换的量子化

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the application of graph-based non-linear geometric transformations for quantum computation, aiming to enhance computational efficiency. Traditional geometric transformations often struggle with complex geometries and require significant computational resources. We propose a novel approach that leverages graph structures to represent geometric transformations, enabling efficient computation and optimization through the integration of quantum entanglement and quantum tuning. The core mechanism centers on constructing a quantum-aware graph model, utilizing graph algorithms for transformation evaluation, and incorporating quantum properties to guide optimization. This paper details the design of a graph-based representation, the implementation of quantum optimization techniques, and the potential benefits of this approach for quantum computing.

DOI: 10.5281/zenodo.22267892

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