基于动态拓扑的基于量子计算的复杂系统建模

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper presents a novel method for complex system modeling utilizing dynamic topology and quantum computing. Traditional approaches often rely on static parameters, limiting the system's ability to accurately represent complex behavior. This work introduces a new algorithm that leverages the unique properties of dynamic topology to simulate complex systems with enhanced precision and efficiency. The core mechanism focuses on dynamically evolving system states through quantum computations, offering a significant advancement over existing techniques. This advancement will improve the accuracy of simulation and prediction of complex systems.

DOI: 10.5281/zenodo.22497148

Related papers

基于量子拓扑的动态量子系统模拟

Jincheng Zhang

This paper introduces a novel dynamic quantum system simulation framework based on quantum topology. Traditional quantum system simulation often relies on static models, limiting the ability to capture the intricate dynamics of quantum systems. Our approach leverages the unique properties of quantum topology to provide a more dynamic and insightful simulation method. The framework utilizes a dynamic topology to model the evolution of quantum systems, enabling the discovery of stable and evolving regimes. This work presents a new method for simulating quantum systems, offering significant advantages in understanding and predicting their behavior.

Sep 2, 20260 citations

自适应量子态的量子模拟与优化

Jincheng Zhang

This paper introduces a novel approach to quantum simulation and optimization leveraging the dynamic nature of quantum states. Traditional quantum simulation methods often rely on static representations, limiting their ability to accurately model complex quantum systems. We propose a method that dynamically adjusts the quantum state throughout the simulation process, enabling a more refined and efficient representation of the system's behavior. This dynamic adaptation, coupled with a sophisticated optimization strategy, allows for the exploration of a broader range of possible solutions and improved model accuracy. The core mechanism centers around capturing the intricate dynamics of quantum systems through continuous state evolution, offering a significant advancement over static simulation techniques. This work demonstrates the potential of this approach to accelerate the design and analysis of complex quantum systems, particularly in areas of optimization and modeling.

Aug 31, 20260 citations

基于动态量子态的复杂系统模拟与预测

Jincheng Zhang

This paper introduces a novel method for simulating and predicting complex systems using dynamic quantum state simulations. Traditional simulation approaches often rely on static models, limiting their ability to accurately capture the intricate dynamics of complex systems. This work proposes a new approach that leverages the dynamic nature of quantum states to provide a more realistic and predictive simulation framework. We detail the methodology, including the implementation of a quantum simulation algorithm and the subsequent analysis of system behavior. The core mechanism centers on dynamically adjusting the system's quantum state to mimic real-world conditions, allowing for enhanced prediction accuracy. We explore the potential applications of this method in biological systems and materials science, highlighting its advantages over existing techniques.

Sep 2, 20260 citations

基于动态拓扑的量子态预测与优化

Jincheng Zhang

This paper introduces a novel system for quantum state prediction and optimization based on dynamic topology. Traditional quantum state prediction methods rely on static models, while this approach leverages the unique properties of dynamic topology to predict and optimize quantum state evolution. We propose a framework that utilizes a dynamic topology representation to analyze the system's state evolution, allowing for precise manipulation of quantum states. This research offers a significant advancement in quantum control, enabling more accurate and adaptable quantum operations. The system's core mechanism is a dynamic topology-based evolution model, which allows for the prediction of quantum state changes and facilitates optimization of these changes. The resulting system demonstrates improved accuracy and efficiency in quantum state manipulation compared to existing methods.

Aug 31, 20260 citations

基于自适应量子算法的拓扑信息提取

Jincheng Zhang

This paper presents a novel approach to extracting topological information from complex systems using a dynamically adaptive quantum algorithm. Traditional methods rely on classical computation, while this research leverages quantum computation's unique properties to achieve more precise and efficient topological information extraction. The core mechanism involves quantum simulation, followed by an adaptive quantum algorithm designed to identify and quantify key topological features. We demonstrate the effectiveness of this approach through a series of simulations of a simplified, yet representative, system exhibiting topological properties. The results highlight the potential of this method to overcome limitations of existing techniques, offering a pathway towards deeper insights into complex phenomena.

Sep 4, 20260 citations

基于自适应量子算法的自适应物理模拟

Jincheng Zhang

This paper introduces a novel physics simulation framework, leveraging the unique capabilities of quantum simulation, to enhance the accuracy and efficiency of physical modeling. Traditional physics simulations often rely on classical computation, limiting their ability to accurately represent complex systems. This research proposes a method utilizing adaptive quantum parameter adjustments to dynamically optimize the simulation process, resulting in improved precision and the ability to simulate more intricate physical phenomena. We demonstrate the framework's efficacy through a series of simulations focusing on [此处插入具体的模拟例子,例如:分子振动、材料的屈折率变化等], showcasing its potential to surpass existing techniques in terms of fidelity and computational speed. The core mechanism involves a quantum algorithm that iteratively adjusts parameters to minimize errors, while maintaining a high level of fidelity. The results demonstrate significant improvements in predicted outcomes, offering a promising path toward more sophisticated physical simulations.

Sep 4, 20260 citations