基于量子纠缠的复杂性函数

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel complexity function based on quantum entanglement, aiming to provide a more accurate assessment of system complexity. Traditional complexity functions often rely on approximations, failing to capture the nuanced intricacies within complex systems. We propose a model leveraging quantum entanglement to simulate system evolution and deliver a precise assessment of complexity. The core mechanism involves mapping the system's evolution to a quantum entangled state, allowing for a more detailed and accurate analysis. This approach offers a significant advancement over existing methods by introducing a fundamental shift in how complexity is quantified and evaluated.

DOI: 10.5281/zenodo.22517208

Related papers

基于量子纠缠的系统动力学模型

Jincheng Zhang

This paper presents a novel system dynamics model based on quantum entanglement, aiming to provide a fundamentally different approach to simulating complex systems. Traditional system dynamics models often rely on classical physics, which can struggle with accurately capturing the intricate dynamics of many real-world systems. This research introduces a quantum entanglement-driven framework, leveraging the unique properties of entanglement to model system evolution and predict future states. The core mechanism involves establishing a dynamic state representation based on entanglement, enabling a more precise and insightful simulation of complex systems compared to classical approaches. The model's potential lies in its ability to capture subtle correlations and emergent behaviors arising from entanglement, offering a new perspective on system dynamics. This work explores the initial implications of this model and outlines avenues for future development.

Sep 1, 20260 citations

基于量子纠缠的拓扑动力学建模

Jincheng Zhang

This paper explores the application of quantum entanglement to model complex systems, focusing on the development of a novel topological dynamic model. Traditional approaches to simulating complex systems often rely on classical physics, limiting their ability to accurately capture intricate dynamics. This research investigates the potential of harnessing quantum entanglement to enhance the simulation of biological molecular reactions and other systems exhibiting dynamic, interconnected behavior. We propose a new model framework that explicitly incorporates quantum entanglement as a fundamental mechanism driving system evolution. This model aims to provide a more precise and insightful understanding of these systems by leveraging the unique properties of entanglement. The core claim is that this model offers a significant advancement in the representation and simulation of complex phenomena, particularly those involving intricate interactions and non-linearity. This work addresses the limitations of conventional methods by introducing a novel approach that directly quantifies and utilizes entanglement as a key component of the simulation process.

Sep 4, 20260 citations

基于量子纠缠的复杂系统模拟

Jincheng Zhang

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.

Aug 31, 20260 citations

基于量子自适应的复杂性评估算法

Jincheng Zhang

This paper introduces a novel complexness assessment algorithm based on quantum self-adaptation. Traditional complexness assessment methods often rely on static metrics, limiting their ability to accurately capture the intricacies of a system. This work proposes a dynamic approach where the algorithm dynamically adjusts complexness metrics based on input data and system state, fostering a more precise and adaptable evaluation process. The core mechanism involves utilizing quantum circuit simulation data to dynamically modify complexness indicators, resulting in improved accuracy. This advancement addresses a critical limitation in current complexness assessment techniques, offering a promising pathway toward more robust and reliable analysis.

Sep 1, 20260 citations

基于动态拓扑的自适应几何的基于量子纠缠的复杂系统分析

Jincheng Zhang

This paper investigates the potential of dynamically-tilted geometry and quantum entanglement to revolutionize system analysis, particularly for complex systems. We propose a novel framework utilizing dynamic topology and quantum entanglement to predict system evolution with enhanced precision. The core mechanism centers around the intricate interplay between these two concepts, enabling a more accurate and nuanced modeling of dynamic systems. Traditional methods often fall short in capturing the complexities inherent in quantum systems, and this work presents a promising approach to overcome these limitations. The resulting system analysis framework offers a fundamentally different perspective, leveraging the strengths of both dynamic topology and quantum entanglement to achieve unprecedented levels of predictive power.

Sep 1, 20260 citations

基于量子态的混沌动力学模拟

Jincheng Zhang

This paper explores the development of a novel approach to chaos simulation using quantum state entanglement. Traditional chaos simulation methods often face limitations in accurately capturing the intricate dynamics of complex systems. This work proposes a quantum state-based simulation technique that leverages the unique properties of quantum entanglement to generate more precise and detailed simulations. We detail the implementation of this method, emphasizing the advantages of quantum entanglement in representing and modeling chaotic behavior. The core mechanism involves establishing correlations between quantum states, allowing for the simulation of non-linear dynamics and intricate feedback loops characteristic of chaotic systems. This offers a significant improvement over existing classical simulation techniques and holds the potential for more accurate predictions within the realm of complex dynamical systems. This research represents a fundamental shift in approach, utilizing the power of quantum mechanics to enhance the simulation of chaotic phenomena.

Sep 7, 20260 citations