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基于量子纠缠的复杂系统模拟

Jincheng Zhang

Published Aug 31, 2026• 0 citations
Publisher

Abstract

This paper proposes a novel approach to complex system simulation utilizing the principles of quantum entanglement. Traditional simulation methods often struggle with accurately capturing intricate interactions within complex systems due to computational limitations and the inherent difficulty of representing quantum phenomena. We introduce a new method predicated on quantum entanglement, offering a pathway towards significantly improved modeling accuracy and the potential for simulating systems previously intractable using classical approaches. The core of this method lies in leveraging entanglement to establish a more fundamental and nuanced understanding of system dynamics. This paper details the theoretical framework, implementation of the core mechanism, and preliminary results demonstrating the method's potential for advancing complex system simulation. We discuss the challenges and future directions of this research.

DOI
10.5281/zenodo.22199943

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This paper investigates the potential of quantum entanglement to significantly improve the efficiency of complex system simulations. Traditional simulation methods often struggle with computational complexity, limiting the exploration of intricate systems. We propose a novel algorithm based on quantum entanglement, aiming to overcome these limitations by enabling the efficient representation and manipulation of quantum states crucial for simulating complex phenomena. The core mechanism of this approach involves utilizing entanglement to represent and evolve the state of a simulated system, resulting in substantial efficiency gains compared to classical simulation techniques. The paper details the proposed algorithm, its mathematical foundations, and preliminary results demonstrating improved simulation speed and accuracy for a specific complex system. We conclude with a discussion of future research directions and the potential impact of this approach on various fields requiring complex system modeling.
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