基于多尺度量子过程的计算模型

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

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.

DOI: 10.5281/zenodo.22203792

Related papers

Title: Quantum Entanglement-Based Complex System Dynamics Modeling Framework

Jincheng Zhang

This paper presents a novel framework for complex system dynamics modeling, leveraging the unique properties of quantum entanglement to achieve significantly enhanced simulation efficiency and predictive accuracy. Traditional methods often struggle with computational limitations when modeling complex systems, while quantum entanglement offers a potential solution for accelerating these simulations. We propose a framework centered around quantum entanglement as a computational unit, meticulously designed to effectively simulate system evolution and exploit entanglement effects for accelerated computations. The core mechanism involves constructing a dynamic system model utilizing entangled qubit states, allowing for parallel processing and a more accurate representation of system behavior. This research explores the practical implications of this approach, detailing its design, implementation, and preliminary results demonstrating improved simulation speed and predictive capabilities compared to existing methods.

Sep 5, 20260 citations

基于多层量子叠加的复杂系统模拟

Jincheng Zhang

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.

Aug 29, 20260 citations

基于量子纠缠的概率计算框架

Jincheng Zhang

This paper introduces a novel probabilistic calculation framework predicated on the principles of quantum entanglement. We propose a system for analyzing complex systems by leveraging the dynamic nature of entangled states to simulate probabilistic processes and predict outcomes. The framework emphasizes the unique capabilities of quantum entanglement in providing a fundamentally different approach to probability assessment compared to traditional, classical methods. This research explores the potential of quantum entanglement to enhance the accuracy and efficiency of probabilistic modeling, offering a fundamentally new perspective on the limitations of classical probability calculations. The core mechanism involves the creation and manipulation of entangled states, followed by a sophisticated algorithm to extract meaningful predictions from these states. This work lays the groundwork for a more robust and potentially transformative approach to probabilistic analysis across a range of complex systems.

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

自适应几何的量子态的计算

Jincheng Zhang

This paper explores the potential of utilizing dynamic adaptive mechanisms to enhance the calculation of quantum states. Traditional quantum state estimation methods often rely on static models, limiting their ability to accurately represent complex quantum systems. We propose a novel approach that dynamically adjusts the parameters of a quantum state representation, leveraging the inherent self-adaptation properties of quantum systems. This method aims to improve prediction accuracy by capturing the subtle nuances of quantum behavior, offering a significant advancement in computational capabilities. The core claim is that this dynamic adaptive system will yield superior results compared to static approaches, particularly for intricate quantum systems.

Sep 1, 20260 citations