Quantum Entanglement-Based Non-Linear Optimization Algorithm

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel non-linear optimization algorithm based on quantum entanglement principles. Traditional optimization methods often struggle with complex, non-linear problems, demanding extensive manual tuning. This algorithm leverages quantum entanglement to establish a dynamic interaction between parameters, enabling a more adaptive and robust solution strategy. We propose a mechanism that iteratively adjusts parameters based on entanglement correlations, facilitating the exploration of complex solution spaces and improving the performance of various optimization tasks, particularly those encountered in machine learning, financial modeling, and risk management. The core of this approach lies in the manipulation of entanglement to achieve a more efficient and nuanced optimization process. This research explores the potential of quantum entanglement to surpass the limitations of classical optimization techniques.

DOI: 10.5281/zenodo.22201542

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