基于量子纠缠的拓扑几何的自适应优化算法

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper introduces a novel optimization algorithm based on quantum entanglement, termed "Quantum-Adaptive Topological Optimization" (QATO). Traditional optimization methods often rely on static parameters, limiting their adaptability and potential for improved performance. QATO leverages the unique properties of quantum entanglement to dynamically adjust the optimization landscape, resulting in enhanced solution quality and speed. The algorithm employs a quantum state representation to capture the inherent structure of the problem, allowing for a continuous, self-correcting optimization process. The core mechanism involves the creation of entangled states, which evolve as the optimization progresses, guiding the algorithm towards optimal solutions. This approach significantly surpasses the capabilities of conventional optimization techniques, particularly in complex and non-convex optimization problems. This work presents a theoretical framework and initial experimental validation demonstrating the effectiveness of QATO in several benchmark optimization tasks.

DOI: 10.5281/zenodo.22157699

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