基于自适应的几何变换与量子纠缠的动态演化

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores a novel framework combining adaptive geometric transformations with quantum entanglement to dynamically evolve and model geometric transformations. Traditional geometric transformations often require manual design, limiting efficiency and precision. This work proposes a dynamic transformation system leveraging self-adaptive geometric transformations and quantum entanglement to achieve real-time, efficient, and accurate geometric modeling. The core mechanism involves a self-tuning geometric transformation process guided by the inherent properties of quantum entanglement, enabling unprecedented control over geometric evolution. We demonstrate the framework's effectiveness through simulations, showcasing significant improvements in computational speed and accuracy compared to conventional methods.

DOI: 10.5281/zenodo.22197555

Related papers

基于量子纠缠的自适应几何变换算法

Jincheng Zhang

This paper introduces a novel algorithm based on quantum entanglement to facilitate adaptive geometric transformations. Traditional geometric transformations often rely on fixed parameters and lack the flexibility to handle intricate transformations. We propose a system leveraging quantum entanglement to dynamically adjust geometric parameters, optimizing the transformation process for enhanced efficiency and precision. The core mechanism involves creating and manipulating entangled states to guide the transformation, offering a fundamentally different approach compared to conventional methods. The algorithm demonstrates significant improvements in geometric transformation accuracy and speed through the application of quantum entanglement. This work presents a promising pathway for advancing geometric computation, particularly in scenarios requiring complex transformations and high fidelity.

Sep 4, 20260 citations

Dynamic Quantum Adaptive Geometry Transformation Guided by Entanglement

Jincheng Zhang

This paper investigates the application of dynamic quantum adaptive geometry transformations guided by entanglement to achieve precise geometric control. Traditional geometric transformations often require iterative refinement, which can be computationally expensive and time-consuming. We propose a novel algorithm that leverages quantum entanglement to dynamically adjust geometric parameters, enabling a significantly reduced number of iterations and improved accuracy. This approach addresses the limitations of conventional methods by offering a fundamentally different approach to geometric manipulation. The core mechanism involves creating and manipulating entangled quantum states to represent the transformation parameters, allowing for precise control and efficient adaptation. We present a theoretical framework and initial implementation demonstrating the algorithm's potential for enhanced geometric precision. The results showcase improved efficiency and accuracy compared to existing methods.

Sep 3, 20260 citations

基于量子计算的自适应几何建模

Jincheng Zhang

This paper introduces a novel approach to geometric modeling utilizing quantum computing for adaptive parameter adjustment. Traditional geometric modeling methods rely heavily on manual parameter tuning, often leading to suboptimal results and increased computational cost. We propose a quantum-enhanced modeling framework that leverages quantum phenomena to dynamically adjust geometric parameters during the modeling process. The core mechanism involves utilizing quantum state manipulation to represent and evolve geometric parameters, enabling automated parameter optimization and ultimately, improved model accuracy and efficiency. We demonstrate the effectiveness of this approach through a series of simulations and analysis, showcasing significant gains in precision and computational speed compared to traditional methods. The paper concludes with a discussion of the potential applications and future research directions.

Aug 31, 20260 citations

Dynamic Quantum Adaptive Geometry Transformation

Jincheng Zhang

This paper presents a novel geometric transformation algorithm based on dynamic quantum entanglement. Traditional geometric transformation methods often rely on manual adjustments, limiting their efficiency and adaptability. We propose a system that leverages quantum entanglement to automatically adjust transformation parameters, enabling optimized geometric modeling and design. The core mechanism centers around the manipulation of entangled quantum states to achieve a continuous and adaptive transformation process. This approach offers a potential paradigm shift in geometric computation, demonstrating enhanced efficiency and versatility compared to conventional methods. The algorithm is designed to handle complex geometric transformations with minimal human intervention.

Sep 3, 20260 citations

Dynamic Adaptive Quantum Entanglement Modeling

Jincheng Zhang

This paper introduces a dynamic adaptive quantum entanglement modeling approach designed to address the challenges of accurately analyzing and predicting quantum entanglement. Traditional geometric modeling techniques often struggle to effectively handle the inherent dynamism of quantum systems, leading to approximations and limitations in our understanding of entangled states. Our proposed method leverages a continuous, self-adjusting geometric transformation process, enabling the system to dynamically adapt to the evolving nature of the entangled state. This adaptive approach promises significantly improved precision and robustness in entanglement analysis and forecasting. The core mechanism centers around employing a series of geometric transformations that are continuously refined based on the observed quantum entanglement characteristics.

Sep 3, 20260 citations

基于量子纠缠的几何变换的自适应算法

Jincheng Zhang

This paper introduces a novel adaptive algorithm for geometric transformations based on quantum entanglement principles. Traditional geometric algorithms often rely on computationally expensive methods, limiting their applicability to complex shapes and simulations. We propose a system utilizing quantum entanglement to dynamically adjust geometric transformation parameters, thereby improving numerical accuracy and computational efficiency. The core mechanism leverages the entanglement to create a dynamic, multi-dimensional representation of the geometric transformation, allowing for rapid and precise adjustments. The algorithm's effectiveness is demonstrated through simulations exploring complex geometric transformations and highlighting its potential to overcome the limitations of conventional approaches. This work explores the theoretical and practical implications of quantum entanglement in geometric computations, proposing a paradigm shift towards more efficient and versatile geometric modeling.

Aug 29, 20260 citations