Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Adaptive Quantum Potential Calculation

Jincheng Zhang

Published Sep 3, 2026• 0 citations
Publisher

Abstract

This paper explores the development of an adaptive quantum potential calculation algorithm, designed to automatically adjust quantum potential calculation parameters for enhanced precision and efficiency. The core mechanism centers around leveraging the dynamic nature of quantum potential calculations to optimize the process, offering a significant advancement over traditional approaches. The research investigates the potential benefits of this adaptive approach through rigorous analysis and preliminary simulations.

DOI
10.5281/zenodo.22267795

Related papers

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, 2026•0 citations

Quantum Potential Calculation: An Adaptive Dynamic Optimization Approach

Jincheng Zhang

Quantum potential calculations are a cornerstone of quantum chemistry and materials science, underpinning the design and understanding of novel molecular structures and materials. However, these calculations often suffer from inherent instability and require meticulous parameter tuning. This paper presents a novel approach to quantum potential calculation – an Adaptive Dynamic Optimization (ADO) method – designed to automatically adjust optimization parameters, thereby significantly enhancing both the accuracy and efficiency of the process. The core mechanism of this algorithm centers around a dynamically adapting optimization loop that iteratively refines the solution space, leading to improved results. The existing literature lacks a comprehensive and rigorously validated approach to dynamically optimize quantum potential calculations, presenting a substantial gap in this area. This work demonstrates the efficacy of the ADO method through extensive simulations and analysis, showcasing its potential to overcome the limitations of traditional methods. The paper concludes with a discussion of the algorithm's strengths, limitations, and potential applications in diverse quantum chemistry scenarios.

•Aug 31, 2026•0 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, 2026•0 citations

Quantum Potential Adaptive Computation

Jincheng Zhang

Quantum Potential Adaptive Computation explores the potential of leveraging quantum potential fields for enhanced computational capabilities. This research introduces a novel framework predicated on quantum potential self-similarity, aiming to automate complex quantum computations through dynamic parameter adjustment. The core mechanism centers around a self-consistent update of the quantum potential, iteratively refining the system's state to achieve desired results. This approach offers a pathway towards simplified and more efficient quantum algorithm design, particularly for problems exhibiting intricate quantum dynamics. We present a preliminary model demonstrating the potential of this framework, highlighting its advantages over traditional methods in certain scenarios. This work investigates the design of a quantum potential adaptive computation framework, outlining its theoretical foundation and preliminary experimental validation.

•Aug 29, 2026•0 citations

Dynamic Quantum Self-Organization Computation

Jincheng Zhang

This paper introduces a novel dynamic quantum self-organization computation algorithm designed to automatically adjust computational parameters for optimal efficiency and results. Traditional self-organization algorithms often require extensive manual parameter tuning, limiting their adaptability and potential for performance improvement. Our proposed algorithm leverages the inherent dynamics of quantum self-organization to dynamically optimize these parameters, offering a significant advancement in this field. We present a detailed explanation of the algorithm's core mechanism, outlining its iterative adjustment process and the resulting benefits for computational efficiency and outcome quality. The paper concludes with a discussion of the implications of this approach and potential future research directions.

•Sep 3, 2026•0 citations

基于动态的量子势的计算

Jincheng Zhang

This paper presents a novel computational framework for quantum systems, leveraging dynamic quantum potentials to automatically adjust system parameters and enhance computational efficiency. Traditional approaches often rely on static quantum potential calculations, which suffer from inherent errors due to this static nature. This work introduces a method that dynamically adjusts parameters based on the evolving quantum potential, mitigating these errors and offering a significant improvement in the performance of quantum computations. The framework's core mechanism involves a continuous optimization process applied to the quantum potential, leading to more accurate and efficient results. We detail the algorithm, its implementation, and preliminary results demonstrating its effectiveness in a specific quantum simulation scenario.

•Aug 29, 2026•0 citations
This paper explores the development of an adaptive quantum potential calculation algorithm, designed to automatically adjust quantum potential calculation parameters for enhanced precision and efficiency. The core mechanism centers around leveraging the dynamic nature of quantum potential calculations to optimize the process, offering a significant advancement over traditional approaches. The research investigates the potential benefits of this adaptive approach through rigorous analysis and preliminary simulations.
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.
Quantum potential calculations are a cornerstone of quantum chemistry and materials science, underpinning the design and understanding of novel molecular structures and materials. However, these calculations often suffer from inherent instability and require meticulous parameter tuning. This paper presents a novel approach to quantum potential calculation – an Adaptive Dynamic Optimization (ADO) method – designed to automatically adjust optimization parameters, thereby significantly enhancing both the accuracy and efficiency of the process. The core mechanism of this algorithm centers around a dynamically adapting optimization loop that iteratively refines the solution space, leading to improved results. The existing literature lacks a comprehensive and rigorously validated approach to dynamically optimize quantum potential calculations, presenting a substantial gap in this area. This work demonstrates the efficacy of the ADO method through extensive simulations and analysis, showcasing its potential to overcome the limitations of traditional methods. The paper concludes with a discussion of the algorithm's strengths, limitations, and potential applications in diverse quantum chemistry scenarios.
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.
Quantum Potential Adaptive Computation explores the potential of leveraging quantum potential fields for enhanced computational capabilities. This research introduces a novel framework predicated on quantum potential self-similarity, aiming to automate complex quantum computations through dynamic parameter adjustment. The core mechanism centers around a self-consistent update of the quantum potential, iteratively refining the system's state to achieve desired results. This approach offers a pathway towards simplified and more efficient quantum algorithm design, particularly for problems exhibiting intricate quantum dynamics. We present a preliminary model demonstrating the potential of this framework, highlighting its advantages over traditional methods in certain scenarios. This work investigates the design of a quantum potential adaptive computation framework, outlining its theoretical foundation and preliminary experimental validation.
This paper introduces a novel dynamic quantum self-organization computation algorithm designed to automatically adjust computational parameters for optimal efficiency and results. Traditional self-organization algorithms often require extensive manual parameter tuning, limiting their adaptability and potential for performance improvement. Our proposed algorithm leverages the inherent dynamics of quantum self-organization to dynamically optimize these parameters, offering a significant advancement in this field. We present a detailed explanation of the algorithm's core mechanism, outlining its iterative adjustment process and the resulting benefits for computational efficiency and outcome quality. The paper concludes with a discussion of the implications of this approach and potential future research directions.
This paper presents a novel computational framework for quantum systems, leveraging dynamic quantum potentials to automatically adjust system parameters and enhance computational efficiency. Traditional approaches often rely on static quantum potential calculations, which suffer from inherent errors due to this static nature. This work introduces a method that dynamically adjusts parameters based on the evolving quantum potential, mitigating these errors and offering a significant improvement in the performance of quantum computations. The framework's core mechanism involves a continuous optimization process applied to the quantum potential, leading to more accurate and efficient results. We detail the algorithm, its implementation, and preliminary results demonstrating its effectiveness in a specific quantum simulation scenario.