Adaptive Quantum Entanglement-Based Optimization Algorithm

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Abstract: This paper introduces an innovative optimization algorithm based on adaptive quantum entanglement. Traditional optimization methods rely on classical physics, while this approach leverages the unique properties of quantum entanglement to provide a novel and potentially more efficient method for solving complex optimization problems. The core mechanism involves predicting optimization trajectories through quantum entanglement and utilizing this prediction to guide the search process. This work explores the potential of this algorithm to address challenges in various fields, particularly those involving intricate systems and large-scale computations. The proposed algorithm demonstrates promise in achieving superior performance compared to existing classical optimization techniques.

DOI: 10.5281/zenodo.22183386

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