Quantum Entanglement-Driven Geometric Optimization – Algorithm Design

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement offers a unique paradigm for computational exploration, potentially enabling breakthroughs in complex optimization problems. This paper explores the design of a novel geometric optimization algorithm leveraging the inherent properties of quantum entanglement to achieve enhanced precision and efficiency. We propose a new approach that dynamically adjusts geometric parameters using entangled qubit states, allowing for a fundamentally different optimization process compared to classical methods. The core mechanism centers around the exploitation of entanglement to create a parallel exploration of the solution space, resulting in a more robust and adaptable optimization strategy. The research investigates the potential of this algorithm to tackle challenging geometric optimization tasks, specifically focusing on the design of a self-adaptive system for parameter tuning within complex shapes. The paper concludes with a discussion of the theoretical implications and preliminary experimental results demonstrating the algorithm's efficacy.

DOI: 10.5281/zenodo.22205257

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