基于自适应量子势的非线性计算

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the development of a novel non-linear computational framework based on adaptive quantum potentials. Traditional approaches to quantum computation and simulation often rely on fixed parameters, limiting efficiency and accuracy. We propose a dynamic adjustment mechanism for the quantum potential, enabling the framework to tailor its influence to specific quantum systems and optimize computational performance. This design addresses the limitations of static methods by providing a mechanism for continuous refinement and adjustment of the potential field, ultimately leading to enhanced precision and efficiency. The core mechanism involves iterative refinement of the quantum potential through a feedback loop, adapting to the unique characteristics of each target system. This paper details the architecture of the framework, discusses its theoretical foundations and practical implementation, and provides a preliminary evaluation of its effectiveness in simulating specific quantum systems. The ultimate goal is to establish a robust and adaptable computational paradigm for tackling complex quantum problems.

DOI: 10.5281/zenodo.22192574

Related papers

Title: Adaptive Quantum Potential Dynamic Computation Framework

Jincheng Zhang

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.

Aug 31, 20260 citations

Based on Adaptive Quantum Potential Calculation

Jincheng Zhang

This paper introduces a novel adaptive quantum potential calculation method, leveraging the unique properties of quantum potential calculations to optimize quantum computation performance and achieve more efficient results. Traditional quantum potential calculation methods rely on static, pre-determined potentials, while our approach emphasizes dynamic optimization to significantly enhance computational accuracy. We explore a method for iteratively refining the quantum potential landscape, enabling a more precise and efficient exploration of the solution space. The core mechanism revolves around a feedback loop that continuously adjusts the potential based on the outcome of the computation, leading to improved results. The proposed method offers a promising avenue for accelerating quantum algorithm development and improving the performance of existing 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

Adaptive Quantum Potential Field Simulation

Jincheng Zhang

This paper introduces an adaptive quantum potential field simulation framework designed to dynamically adjust simulation parameters and achieve more accurate results. Traditional quantum potential field simulations often rely on static parameter settings, a limitation that hinders the ability to capture complex quantum phenomena. This work proposes a novel approach utilizing the dynamic nature of quantum potential fields to iteratively refine simulation parameters, ultimately leading to improved fidelity and insight. The core mechanism involves a continuous, iterative process of parameter adjustment guided by the inherent characteristics of the quantum field. We demonstrate the framework's efficacy through simulations of a specific quantum system, highlighting its ability to overcome limitations of static approaches. This innovation offers a significant step towards more robust and insightful quantum simulation.

Sep 6, 20260 citations

自适应量子态的非线性量子纠缠计算

Jincheng Zhang

This paper introduces a novel framework for quantum state manipulation, specifically focused on the dynamic adjustment of quantum state to enhance computational accuracy. Traditional approaches to quantum state computation often rely on fixed, static parameters, limiting their adaptability to complex input data. We propose a system based on self-adaptive entanglement adjustment, which dynamically adjusts the entanglement between qubits to achieve improved precision in calculations. The core mechanism leverages a feedback loop to optimize the entanglement state, mitigating the limitations of static parameterization. This research explores the potential of this approach to tackle challenges in quantum computation, particularly in scenarios involving dynamic data and intricate entanglement structures. The paper details the theoretical foundation, implementation architecture, and preliminary results demonstrating the effectiveness of this self-adaptive system. The key contribution is a novel algorithm for self-adaptation and a detailed analysis of its impact on computational performance. We believe this framework represents a significant step towards more robust and adaptable quantum computation.

Aug 31, 20260 citations

基于动态量子势场的自适应优化算法

Jincheng Zhang

Quantum computing holds immense promise for revolutionizing various fields, and the development of efficient quantum algorithms is crucial for realizing this potential. However, the design and optimization of quantum circuits often involve complex parameter settings, which can be time-consuming and resource-intensive. This paper proposes a novel optimization algorithm based on dynamically adjusted quantum potentials. We introduce a method that automatically tunes the parameters of a quantum potential field, allowing the algorithm to adapt to the specific characteristics of the problem and improve both computational efficiency and accuracy. The core mechanism involves iteratively adjusting the potential field based on a feedback loop, leading to a more robust and adaptable optimization process. This work presents a new approach to quantum optimization that addresses limitations of traditional methods.

Sep 4, 20260 citations