基于多层量子叠加的复杂系统模拟

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper explores a novel approach to simulating complex systems by leveraging the principles of multi-layered quantum superposition. Traditional methods of simulating complex systems often struggle with computational limitations and inaccuracies, particularly when dealing with systems exhibiting intricate dynamics and numerous interacting variables. The proposed methodology addresses these challenges by decomposing the complex system into multiple quantum superposition layers. Each layer represents a specific state or aspect of the system, and the superposition principle allows for the simultaneous exploration of multiple potential states, dramatically enhancing the accuracy and efficiency of the simulation. The core claim of this research is the ability to achieve precise prediction of system behavior through this layered superposition approach. The underlying mechanism involves exploiting the inherent parallelism offered by quantum superposition, offering a fundamentally different paradigm for complex system modeling. This work establishes a new methodology for complex system simulation, promising significant advancements across various scientific and engineering domains. The presented framework provides a theoretical foundation and a conceptual design for implementing such simulations, highlighting the potential for future development and experimental validation.

DOI: 10.5281/zenodo.22152722

Related papers

基于量子神经网络的复杂系统建模

Jincheng Zhang

This paper explores the potential of Quantum Neural Networks (QNNs) for modeling and simulating complex systems. Traditional neural networks often struggle with the inherent non-linearity and high dimensionality characteristic of complex systems, leading to inaccuracies and computational challenges. QNNs, leveraging principles of quantum mechanics such as superposition and entanglement, offer a fundamentally different approach that may overcome these limitations. This work outlines a framework for constructing a QNN specifically designed for complex system modeling, detailing the key components and potential advantages. The core claim is that QNNs can provide a more accurate and efficient method for modeling and simulating complex systems. We introduce a novel mechanism utilizing quantum properties for enhanced representation and processing of complex system dynamics. This represents a new approach to complex system modeling, offering the possibility of uncovering previously hidden patterns and behaviors. This document focuses on the theoretical foundation and design principles of this approach, without involving experimental validation.

Sep 5, 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

Quantum-Based Non-Linear System Dynamics Simulation

Jincheng Zhang

This paper presents a novel approach to non-linear system dynamics simulation utilizing quantum computing principles. Traditional simulation methods often struggle to effectively model complex systems due to computational limitations and inherent complexities. We introduce a quantum-enhanced simulation framework that leverages quantum mechanics to achieve unprecedented levels of accuracy and efficiency. The core mechanism involves a quantum-based model representation and a sophisticated optimization algorithm, designed to address the challenges inherent in simulating intricate dynamic systems. This work demonstrates the potential of quantum computing to revolutionize system dynamics modeling, offering a pathway to more precise and efficient simulations compared to classical methods. The paper explores the theoretical foundations of this approach, details the implementation of the simulation framework, and presents preliminary results showcasing the benefits of quantum-enhanced simulation.

Sep 4, 20260 citations

Quantum-Enhanced Simulation of Dynamical Systems

Jincheng Zhang

Quantum-enhanced simulation offers a paradigm shift in the modeling and understanding of complex dynamical systems, particularly those exhibiting inherent quantum behavior. This paper explores the potential of leveraging quantum algorithms to dramatically improve the accuracy and efficiency of simulations, addressing the limitations of classical methods in modeling systems with strong correlations and inherent quantum fluctuations. We propose a novel approach to quantum simulation, focusing on the integration of quantum entanglement and superposition to overcome inherent computational bottlenecks. The core claim is that this method can achieve significantly enhanced accuracy and representability compared to current classical methods, holding the potential for transformative breakthroughs across diverse fields, including physics, chemistry, and materials science. This work details the theoretical framework, algorithm design, and preliminary experimental validation demonstrating the feasibility of this approach.

Aug 31, 20260 citations

基于多尺度量子过程的计算模型

Jincheng Zhang

This research explores the development of a novel computational model predicated on the principles of quantum processes. The core aim is to create a framework capable of simulating complex systems' evolution and behavior with enhanced accuracy and predictive power compared to existing computational methods. We propose a model that leverages the inherent characteristics of quantum processes – such as superposition, entanglement, and tunneling – to provide a more nuanced and realistic representation of system dynamics. This model will be designed to quantitatively assess and predict outcomes across a range of scenarios, offering a significant advancement in the analysis of dynamic systems. This work addresses the limitations of current approaches by explicitly incorporating quantum mechanical elements into a computational framework.

Aug 31, 20260 citations

基于量子算法的混沌动力学模拟

Jincheng Zhang

This paper introduces a novel framework for chaos dynamics simulation utilizing quantum algorithms. Traditional methods for simulating complex nonlinear dynamical systems are computationally prohibitive, limiting their applicability to intricate scenarios. We propose a quantum-enhanced approach that leverages quantum computation's inherent parallelism to drastically accelerate and refine the simulation process. This framework focuses on generating a robust and stable representation of chaotic behavior, offering a significant advancement over existing techniques. The core mechanism involves quantum state manipulation to represent and evolve the system's state, coupled with optimized simulation algorithms. The paper details the design and implementation of this quantum simulation method, including a discussion of the potential benefits and challenges associated with its application. The resulting simulation results demonstrate enhanced accuracy and stability in modeling chaotic systems, offering a new pathway for understanding and predicting complex dynamical behaviors.

Sep 4, 20260 citations