Quantum Entanglement-Guided Geometric Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum entanglement, a peculiar quantum phenomenon, offers a potentially transformative approach to optimization problems. This paper explores the utilization of quantum entanglement to accelerate geometric optimization algorithms, specifically focusing on a novel computational geometry approach. We propose a method leveraging entangled qubit states to enhance the convergence speed and efficiency of optimization procedures. The core claim centers around the demonstration of a significant speedup compared to classical optimization techniques, achieved through the application of quantum entanglement. The investigation will detail the fundamental principles behind this approach, present preliminary experimental results, and discuss potential future directions for research and development.

DOI: 10.5281/zenodo.22302753

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