基于自适应量子纠缠的非线性几何优化

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper presents a novel approach to non-linear geometric optimization utilizing the principles of quantum entanglement. Traditional optimization algorithms often struggle with complex, high-dimensional spaces, particularly when dealing with non-convex problems. We introduce a new algorithm, termed "Quantum Geometric Optimization" (QGO), which leverages the unique properties of quantum entanglement to enhance the search process and achieve solutions that are significantly more robust and efficient than conventional methods. The core of QGO lies in the creation of a 'quantum-entangled search space' where the optimization landscape exhibits inherent non-linearity. By employing specific entanglement operations, we enable a novel approach to global search, surpassing the capabilities of existing algorithms. We demonstrate the effectiveness of QGO through the solution of a complex, high-dimensional linear programming problem, achieving a substantially improved solution compared to state-of-the-art algorithms. The core mechanism focuses on manipulating entanglement to induce a "structured search" that effectively explores the solution space. The results underscore the potential of quantum entanglement as a powerful tool for tackling challenging optimization problems.

DOI: 10.5281/zenodo.22635565

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