Self-Spin Quantum Entanglement Optimization – Topological Structure Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This research explores the application of self-spin quantum entanglement to a novel geometric optimization algorithm. Traditional geometric optimization methods often rely on classical algorithms and can struggle with complex geometries. We propose a new approach utilizing the unique properties of quantum entanglement to dynamically adjust geometric parameters, enabling a more efficient and accurate optimization process. The core mechanism involves utilizing self-induced quantum entanglement to guide the exploration of the solution space, effectively creating a 'quantum guide' to fine-tune the geometric form. This investigation focuses on the potential of this technique to surpass the limitations of existing geometric optimization methods and offers a promising pathway toward enhanced precision and speed.

DOI: 10.5281/zenodo.22205314

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