基于量子态的自适应几何优化

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel approach to geometric optimization leveraging the inherent quantum state properties of the problem. Traditional geometric optimization methods often require manual parameter tuning, limiting precision and requiring significant computational effort. We propose a quantum-inspired algorithm that utilizes quantum state transformations to dynamically adjust parameters during the optimization process, leading to improved geometric accuracy and robustness. The core mechanism involves mapping the problem's objective function to a quantum state space, allowing for efficient exploration of the solution space. We demonstrate the efficacy of this method through a series of numerical experiments, showcasing significant improvements in geometric accuracy compared to standard optimization techniques. The algorithm's ability to adapt to the problem's characteristics makes it a promising tool for complex geometric optimization challenges.

DOI: 10.5281/zenodo.22199200

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