Nonlinear Geometry Field Theory Dynamic Parameter Adjustment Based on Quantum Computing

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the application of a novel dynamic parameter adjustment framework within Nonlinear Geometry Field Theory (NGFT) to simulate complex systems. The core claim is that leveraging quantum computing's inherent advantages—particularly its ability to perform precise iterations and handle complex systems—will significantly enhance the accuracy and adaptability of parameter adjustment processes. The proposed framework utilizes quantum algorithms to dynamically adjust parameters based on environmental feedback, offering a potential paradigm shift in the simulation and modeling of intricate systems. This work addresses the limitations of traditional parameter adjustment methods and highlights the transformative potential of quantum computing in this domain.

DOI: 10.5281/zenodo.22185410

Related papers

Nonlinear Geometry Field Theory Simulation Based on Quantum Computing

Jincheng Zhang

This paper presents a novel simulation framework for nonlinear geometry field theory, leveraging quantum computing to achieve high-fidelity simulations of complex physical phenomena. We outline the core mechanism, emphasizing the integration of non-linear geometry field theory with quantum computation to accelerate the simulation process. The paper argues that this approach offers a significant advancement over traditional methods, particularly in handling intricate geometric configurations and enabling high-precision simulations. The development of this framework is driven by the potential of quantum computing to overcome limitations in computational complexity, offering improved accuracy and efficiency for modeling diverse physical systems. The core claim is that this simulation framework can accurately represent and simulate complex geometric phenomena with enhanced precision.

Aug 31, 20260 citations

Dynamic Topology Geometry for Quantum Computing Speed Optimization

Jincheng Zhang

This paper explores the application of dynamic topology geometry to accelerate quantum computation. Traditional quantum algorithms often rely on manually adjusting parameters, which can be computationally expensive and limit scalability. We propose a novel algorithm that dynamically adjusts geometric parameters during the simulation process, effectively reducing the computational complexity. This approach leverages the inherent non-linearity within the system to achieve efficient and optimized performance. The core mechanism centers around a transformation of the simulation process into a parameter-adjustment iterative loop, mitigating the limitations of static optimization techniques. The investigation demonstrates significant improvements in simulation time and accuracy compared to traditional methods, highlighting the potential for substantial speedups in quantum algorithm design.

Sep 3, 20260 citations

Quantum Adaptive Spacetime Geometry

Jincheng Zhang

This paper explores the potential of quantum self-adaptive spacetime geometry – a novel computational framework leveraging quantum entanglement to dynamically adjust spacetime parameters and enable precise spacetime modeling. Traditional approaches to spacetime simulation rely on manually designed geometries, limiting their adaptability. We propose a mechanism that dynamically adjusts spacetime geometry based on learned data and environmental information, automating the process through a specialized spacetime geometry calculation algorithm. The core claim is that this framework offers a significant advancement in spacetime simulation, enabling more accurate and flexible modeling compared to conventional methods. This work introduces a dynamic spacetime geometry parameter adjustment system, demonstrating its potential for enhanced simulation fidelity and computational efficiency.

Aug 31, 20260 citations

Nonlinear Quantum Adaptive Geometry Modeling

Jincheng Zhang

This paper introduces a novel approach to geometry modeling utilizing nonlinear quantum adaptive geometry transformations. We propose a method that transforms complex geometric models into adaptable geometric models, enabling self-adaptive deformation within the model. The core mechanism involves a quantum process that dynamically adjusts geometric parameters based on a feedback loop, resulting in a highly flexible and responsive model. This approach offers significant advantages over traditional methods, particularly in handling intricate geometries and complex deformations. This research addresses the limitations of existing techniques by introducing a fundamentally new method that leverages quantum mechanics to achieve a level of adaptability not previously attainable. This work demonstrates the feasibility and potential of this approach through mathematical analysis and illustrative examples.

Sep 3, 20260 citations

Quantum Topology Field Theory Simulation Framework for Adaptive Quantum Computing

Jincheng Zhang

Quantum Topology Field Theory (QTTF) offers a novel approach to quantum computation by leveraging the topological properties of spacetime. This paper proposes a self-adaptive quantum computing simulation framework, designed to efficiently model complex systems and dynamically adjust quantum parameters based on simulation results. We introduce a dynamic parameter adjustment mechanism based on the inherent properties of QTTF, enabling precise simulation of quantum behavior and enhancing accuracy compared to traditional static simulation methods. The framework's core principle lies in constructing a quantum computational model that evolves in response to the simulation process, fostering a self-correcting and adaptable simulation methodology. The key contribution of this work is the development of a robust and flexible simulation framework that can effectively handle the intricacies of quantum systems, offering a significant advancement in the field of adaptive quantum computing.

Sep 6, 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