Quantum Entanglement-Adaptive Geometry Parameter Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Entanglement-Adaptive Geometry Parameter Optimization presents a novel algorithm for optimization based on the principles of quantum entanglement. This research explores the potential of utilizing entangled states to dynamically adjust geometric parameters within a system, aiming to achieve enhanced performance through a self-adaptive optimization process. The core mechanism centers around leveraging quantum entanglement to create a system where adjustments to geometric parameters are intrinsically linked to the observed data, leading to a more effective optimization strategy than traditional methods. The proposed approach offers a fundamentally different approach to problem-solving, potentially unlocking new capabilities in diverse fields.

DOI: 10.5281/zenodo.22289414

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