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Quantum Entanglement Optimization of Complex Systems

Jincheng Zhang

Published Sep 7, 2026• 0 citations
Publisher

Abstract

Quantum entanglement, a peculiar phenomenon where two or more particles become linked and share the same fate regardless of the distance separating them, holds significant promise for advanced optimization techniques. This paper explores the application of quantum entanglement to design a novel optimization algorithm, leveraging the unique properties of entangled states to achieve efficient parameter and structural optimization. We propose a quantum-enhanced optimization model that utilizes quantum superposition and entanglement to facilitate a more robust and potentially faster search process, particularly applicable to complex systems like machine learning models and chemical reaction pathways. The core claim centers on the design of an algorithm that exploits the inherent structure of entangled states to guide the optimization process towards optimal solutions. This work aims to provide a new perspective on optimization methodologies, potentially unlocking advancements in diverse fields.

DOI
10.5281/zenodo.22639376

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Quantum entanglement, a peculiar phenomenon where two or more particles become linked in such a way that they share the same fate, has emerged as a powerful tool in quantum physics and increasingly, in various fields like optimization. This paper explores the potential of utilizing quantum entanglement to design novel topological optimization algorithms. We propose a framework that leverages the unique properties of entanglement to construct and manipulate quantum states, ultimately aiming to optimize complex systems by exploring and refining topological structures. The core claim is that by employing this approach, we can overcome limitations of existing algorithms and achieve improved performance in optimization tasks.
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.
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