基于量子纠缠的几何变换的自适应算法

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

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.

DOI: 10.5281/zenodo.22153081

Related papers

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

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

基于量子纠缠的拓扑学几何的自适应建模

Jincheng Zhang

This paper presents a novel approach to geometry modeling utilizing quantum entanglement. The core concept revolves around a dynamically adjustable, quantum-enhanced modeling framework that adapts to input data, allowing for unprecedented precision and efficiency in geometric prediction. We explore the potential of this method to overcome limitations in traditional geometric modeling techniques, particularly in scenarios requiring high accuracy and complex transformations. The paper details the underlying principles of entanglement-based geometry and introduces a robust modeling algorithm, demonstrating its effectiveness through illustrative examples and preliminary results. The focus is on achieving a self-adaptive and computationally efficient method for geometric representation.

Aug 31, 20260 citations

The Adaptive Geometry Transformation Quantum Computing Simulation Algorithm

Jincheng Zhang

This paper introduces an adaptive geometry transformation quantum computing simulation algorithm designed to enhance the accuracy and efficiency of complex system simulations. Traditional quantum computing methods often rely on manual design, limiting the potential for improved simulation fidelity. Our algorithm leverages quantum entanglement and geometric transformations to dynamically adjust the simulation parameters, leading to more precise and robust numerical results. We demonstrate the effectiveness of this approach through a focused simulation of a quantum system exhibiting complex dynamic behavior. The core mechanism involves a novel method for generating and manipulating entangled states, which is then used to iteratively refine the simulation's parameters to achieve optimal accuracy. The algorithm's ability to self-adapt to the system's dynamics represents a significant advancement in quantum computing simulation.

Sep 1, 20260 citations

基于量子拓扑的几何变换算法的动态调整

Jincheng Zhang

This paper explores the application of quantum topology principles to develop a dynamic adjustment algorithm for geometric transformations. Traditional geometric transformations often suffer from inefficient computational costs, particularly for complex shapes. We propose a novel algorithm leveraging quantum topological properties to automate and dynamically adjust these transformations, aiming to enhance computational efficiency. The algorithm employs a quantum entanglement-based optimization strategy to navigate through the complex space of possible geometric adjustments. We detail the key steps involved in the algorithm's design, including the construction of a quantum state representation and the implementation of a self-correcting mechanism. The core claim focuses on achieving significant improvements in computational speed and flexibility compared to traditional geometric transformation techniques. The paper presents a preliminary demonstration of the algorithm's effectiveness in a specific geometric transformation scenario, highlighting its potential for broader applicability.

Sep 3, 20260 citations