Quantum Entanglement-Based Adaptive Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel optimization algorithm based on quantum entanglement principles, designed to achieve more efficient and accurate optimization. Traditional optimization methods often rely on manual parameter tuning, making them unsuitable for dynamically changing data. We propose a quantum entanglement-driven approach that leverages the unique properties of quantum entanglement to provide a rapid and automated assessment of optimization process efficiency and results. This algorithm automatically adjusts optimization parameters, leading to improved performance compared to conventional methods. The core mechanism centers around the construction of a quantum-enhanced evaluation mechanism and an automated parameter adjustment process facilitated by quantum entanglement. This work demonstrates the potential of this approach to significantly enhance optimization capabilities.

DOI: 10.5281/zenodo.22204083

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