Title: Adaptive Quantum Potential Dynamic Computation Framework

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces an adaptive quantum potential dynamic computation framework designed to enhance the accuracy and efficiency of quantum simulations. Traditional computational approaches often rely on static parameter adjustments, limiting the framework's flexibility and potentially hindering optimal results. Our framework dynamically adjusts quantum potential parameters based on the specific characteristics of the computational task, allowing for a more tailored and efficient simulation process. We present a novel mechanism integrating the quantum potential concept with the computational framework, enabling this dynamic adaptation. The core claim is that this framework achieves more accurate and efficient simulations compared to traditional methods, particularly in complex systems. This work addresses the limitations of static parameter adjustments in quantum simulation and offers a promising approach for advanced computational modeling.

DOI: 10.5281/zenodo.22205533

Related papers

Title: Adaptive Quantum Algorithm Optimization of Quantum Systems

Jincheng Zhang

This paper presents a novel quantum system optimization algorithm based on adaptive quantum system dynamics. Traditional optimization methods often rely on fixed parameters, limiting the potential for improved performance. We propose a dynamically adjustable algorithm that leverages the inherent properties of quantum systems to optimize them in real-time. The core mechanism involves a continuous feedback loop that adjusts algorithm parameters based on the quantum system's state, resulting in enhanced efficiency compared to static optimization techniques. This approach addresses the limitations of current algorithms by providing a framework for adapting to the specific characteristics of each quantum system. The design incorporates a novel weighting scheme to determine the impact of each parameter adjustment, ensuring a balanced and effective optimization process. The results demonstrate significant improvements in various quantum system optimization tasks, particularly in scenarios with complex dynamics.

Sep 3, 20260 citations

Title: Adaptive Quantum Simulation Data Analysis

Jincheng Zhang

This paper presents a novel approach to data analysis utilizing adaptive quantum simulation, focusing on achieving high-resolution analysis. Traditional data analysis often relies on fixed parameters, limiting the potential for optimal insights. Our work introduces a self-adjusting parameter adjustment mechanism within a quantum simulation framework, dynamically modifying simulation parameters based on the data analysis results to iteratively enhance the resolution and accuracy of the simulation. The core claim is to develop a self-adaptive quantum simulation framework capable of continuous refinement and improved data analysis through this dynamic parameter adjustment. The study demonstrates the effectiveness of this approach through a series of simulations, showcasing significant improvements in resolution and accuracy compared to traditional methods. The framework is designed to provide a flexible and powerful tool for tackling complex data analysis tasks.

Aug 31, 20260 citations

Title: Adaptive Quantum Field Quantum Simulation for Complex System Modeling

Jincheng Zhang

This paper introduces a novel quantum simulation method based on adaptive quantum fields, designed to effectively model complex quantum systems while offering high accuracy and efficiency. The core mechanism leverages a dynamically adjusted quantum field parameter, automating the simulation process and minimizing computational burden. Traditional quantum simulation often requires manual parameter design, limiting the applicability to relatively simple systems. Our approach addresses this limitation through an automated adjustment system, significantly enhancing simulation capabilities for complex systems. The presented method promises improved efficiency and accuracy compared to existing approaches.

Sep 3, 20260 citations

Title: Adaptive Quantum Algorithm Optimization via Dynamic Quantum Algorithm Tuning

Jincheng Zhang

This paper presents a novel optimization approach for quantum algorithms leveraging dynamic quantum algorithm tuning. Traditional optimization methods often rely on static parameter settings, limiting the potential for efficient algorithm execution. We introduce a dynamic mechanism that automatically adjusts the quantum algorithm's execution characteristics based on learned properties, resulting in accelerated quantum computation. The core of this method lies in a "dynamic quantum algorithm tuning" mechanism, which iteratively refines the algorithm's parameters to achieve optimal performance. This approach addresses the limitations of static optimization techniques by providing a flexible and adaptive optimization strategy. This research explores the potential of dynamically adjusting quantum algorithms to significantly enhance their computational speed.

Sep 3, 20260 citations

Title: Adaptive Quantum Potential Field Simulation

Jincheng Zhang

This paper presents an innovative approach to quantum potential field simulation, leveraging adaptive quantum field parameter adjustment to achieve more accurate and efficient simulations. Traditional methods rely on manual parameter tuning, while this framework utilizes a dynamic adjustment mechanism to automatically optimize the simulation for complex quantum systems. The core of the innovation lies in a self-adjusting system that continuously monitors the system's behavior and modifies the quantum field parameters accordingly, resulting in improved simulation fidelity and enhanced performance in quantum computing. The research demonstrates the effectiveness of this approach through a series of simulations, showcasing significant improvements in the representation of quantum phenomena compared to conventional methods. The paper highlights the benefits of automated parameter adjustment and its potential to accelerate the development of quantum algorithms.

Sep 4, 20260 citations

Title: Adaptive Quantum Computation Algorithm for Topological Optimization

Jincheng Zhang

Quantum computation is rapidly advancing, demanding sophisticated algorithms for simulation and optimization. Traditional methods often rely on manual parameter tuning, limiting scalability and efficiency. This paper introduces an adaptive quantum computation algorithm based on topological optimization, leveraging the non-local properties of quantum entanglement to dynamically model quantum states and enhance the accuracy of physical simulations and numerical calculations. We propose a novel framework that automatically adjusts quantum parameters to optimize performance. This approach offers a significant advancement over existing techniques, providing a robust and automated solution for complex quantum computations. The core mechanism centers around exploiting the inherent dynamics of quantum systems through topological structures, enabling adaptive optimization of quantum states. The algorithm's design directly addresses the limitations of traditional quantum computing by automating the optimization process. This work aims to provide a foundation for developing more efficient and scalable quantum algorithms.

Sep 2, 20260 citations