基于自适应的量子动力学模拟

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel framework for quantum dynamics simulation, leveraging the unique properties of quantum computing to achieve accurate and efficient simulation of complex quantum systems. Traditional approaches often rely on classical computation, limiting the scope and computational cost of these simulations. Our proposed method dynamically adjusts simulation parameters based on quantum principles, thereby enhancing the simulation's fidelity and reducing computational burden. This innovation offers a significant advancement in the field of quantum simulation, particularly for systems requiring high accuracy and efficient processing. The core mechanism centers around exploiting quantum mechanics to guide parameter optimization, leading to improved simulation outcomes.

DOI: 10.5281/zenodo.22459648

Related papers

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

基于动态拓扑的非结构化群的量子模拟

Jincheng Zhang

This paper explores the application of dynamic topology as a framework for quantum simulation, focusing on the development of a novel quantum simulation method utilizing entangled qubits and topological quantum properties. We propose an optimization strategy to enhance the accuracy and efficiency of quantum simulations. Traditional simulation methods often rely on predefined rules, limiting the potential for complex systems. Dynamic topology offers a unique approach, allowing for the inherent flexibility and adaptability necessary for simulating intricate quantum systems. This work investigates the potential of dynamically evolving configurations within a quantum system, incorporating quantum entanglement and topological features to achieve superior simulation fidelity. The core mechanism centers around leveraging the principles of dynamic topology to guide the simulation process, ultimately contributing to improved quantum computations.

Aug 31, 20260 citations

自适应量子势的动态调整与计算

Jincheng Zhang

This paper investigates the development of a novel dynamic adjustment and computation framework centered around adaptive quantum potentials. Traditional computational models rely on static parameters, limiting their ability to accurately model and simulate complex systems. Our approach leverages the inherent dynamism of quantum potential landscapes to dynamically adjust parameters, facilitating precise simulations and high-fidelity calculations. The core mechanism involves constructing a dynamic adjustment framework, employing iterative simulations and parameter optimization to achieve optimal results. We present a preliminary implementation demonstrating the potential for significant computational efficiency gains through this adaptive approach. The paper explores the benefits of this method, focusing on its capacity to handle complex systems and its potential to revolutionize computational modeling.

Sep 4, 20260 citations

基于动态拓扑的基于量子计算的复杂系统建模

Jincheng Zhang

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.

Sep 6, 20260 citations

基于自适应的量子拓扑领域建模

Jincheng Zhang

This paper presents a novel framework for quantum topology modeling, leveraging the unique properties of quantum computation to dynamically adjust model parameters and achieve enhanced accuracy and efficiency in topological field analysis. Traditional methods rely on classical computation, while our approach harnesses quantum mechanics to guide the modeling process. The core mechanism involves strategically manipulating parameters within a quantum system, allowing for a more adaptable and accurate representation of complex topological structures. We demonstrate the framework's effectiveness through simulated examples and discuss its potential impact on advancing topological research.

Sep 6, 20260 citations

Quantum Dynamics Simulations: Adaptive Parameter Adjustment for Enhanced Accuracy and Efficiency

Jincheng Zhang

Quantum dynamics simulations are computationally intensive, requiring careful selection of parameters to achieve accurate results. This paper introduces an adaptive parameter adjustment algorithm for quantum dynamics simulations, designed to automatically adjust parameters based on simulation outcomes. The core mechanism leverages the inherent properties of quantum dynamics to dynamically optimize parameters, thereby improving simulation fidelity and efficiency. The algorithm aims to overcome the limitations of traditional parameter tuning methods, offering a significant advancement in the handling of complex simulations. This work presents a novel approach that adapts simulation parameters in real-time, resulting in enhanced accuracy and computational savings.

Sep 4, 20260 citations