Quantum Entanglement-Driven Non-Linear Optimization Algorithm

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement, a fundamental phenomenon in quantum mechanics, offers a unique potential for advanced optimization algorithms. This paper explores the design and implementation of a novel optimization algorithm leveraging quantum entanglement to address complex systems, particularly machine learning model parameter optimization. We propose a method that decomposes the optimization problem into entangled quantum steps, enabling parallel computation and significantly enhancing efficiency compared to classical optimization techniques. The core mechanism centers around exploiting the correlations inherent in entangled states to guide the search process towards optimal solutions. The research investigates the potential benefits and challenges of employing this approach, highlighting its promise for addressing current bottlenecks in optimization methodologies.

DOI: 10.5281/zenodo.22193847

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