自适应拓扑几何的基于量子计算的几何变换优化算法

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel geometric transformation optimization algorithm leveraging quantum computing principles, specifically focusing on quantum error correction and quantum computation. Traditional optimization techniques often struggle with complex geometric transformations, necessitating manual parameter tuning and limiting the algorithm's adaptability. This work proposes a quantum-enhanced approach that dynamically adjusts parameters based on quantum error correction, enabling a self-adaptive optimization process. The algorithm's core mechanism centers around quantum state manipulation to achieve precise and efficient geometric transformations. The paper details the proposed algorithm's architecture, explores its potential benefits, and presents preliminary results demonstrating its efficacy in a specific geometric transformation problem. The focus is on showcasing the advantages of quantum computation in addressing geometric optimization challenges.

DOI: 10.5281/zenodo.22520917

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