基于量子纠缠的自适应几何模型的动态调整算法

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel algorithm for dynamic adjustment of geometric models based on quantum entanglement. Traditional geometric modeling techniques often struggle to handle complex geometries, requiring significant computational resources. Quantum entanglement offers a potential pathway to accelerate these calculations, allowing for automatic parameter adjustment based on the model's inherent properties. This work proposes a framework that leverages entanglement to efficiently represent and manipulate geometric models, facilitating high-precision and high-throughput modeling and optimization. The core mechanism centers around converting geometric models into a quantum entanglement framework, enabling automated parameter adjustment driven by entanglement properties. This approach promises to significantly enhance geometric modeling capabilities, particularly for complex and intricate shapes.

DOI: 10.5281/zenodo.22606195

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

基于量子纠缠的自适应几何优化

Jincheng Zhang

This paper investigates the potential of leveraging quantum entanglement to revolutionize geometric optimization. Traditional geometric optimization algorithms often struggle with complex, high-dimensional shapes due to computational limitations. We propose a novel algorithm, Quantum Geometry Optimizer (QGO), that utilizes entangled quantum states to represent and manipulate geometric configurations, offering a potential pathway to significantly enhance optimization efficiency and handle intricate shapes. The core mechanism centers around exploiting the unique properties of entanglement to create a dynamic and adaptive optimization process. This work provides a theoretical foundation and preliminary experimental validation demonstrating the efficacy of this approach. The paper concludes with a discussion of future research directions and the implications of this technology for various fields.

Sep 4, 20260 citations

基于量子纠缠的几何结构拓扑优化

Jincheng Zhang

This paper explores the potential of leveraging quantum entanglement to revolutionize geometric structure optimization. Traditional geometric optimization methods often struggle with complex structures and topological problems due to their reliance on classical computational tools. We propose a novel approach utilizing quantum entanglement to construct a dynamic model, allowing for the optimization of complex geometric structures with enhanced precision and the ability to tackle intricate topological problems. This work introduces a framework that leverages entangled states to represent and manipulate geometric configurations, offering a fundamentally different approach to optimization compared to existing techniques. The core mechanism involves establishing a quantum-enhanced representation of the optimization landscape, enabling the exploration of solutions beyond classical parameter spaces. The paper presents a preliminary investigation of this approach, demonstrating its potential for handling challenging geometric problems through quantum-enhanced modeling and optimization.

Sep 4, 20260 citations

基于量子纠缠的非线性几何优化

Jincheng Zhang

This paper investigates the application of quantum entanglement to enhance geometric optimization, specifically focusing on the design and optimization of complex geometric shapes. Traditional optimization methods often rely on classical computation, while entanglement introduces a novel approach to accelerate optimization processes, particularly in high-dimensional geometric problems. We propose a quantum computing algorithm predicated on entangled states, leveraging their unique properties to facilitate deeper geometric transformations. This research explores the potential of quantum entanglement to overcome limitations in classical optimization techniques, offering a promising pathway towards enhanced efficiency and design capabilities. The core mechanism centers on creating entangled states that facilitate the exploration of intricate geometric forms. This paper details the theoretical framework, algorithm implementation, and preliminary experimental results demonstrating the efficacy of this approach.

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

Based Quantum Entanglement of Self-Adaptive Geometric Modeling

Jincheng Zhang

This paper introduces a novel geometric modeling approach based on quantum entanglement, designed to simulate complex geometric structures with high precision and efficiency. The core of the system leverages the unique properties of entangled quantum states to dynamically adjust geometric parameters, allowing for automated optimization of the resulting geometry. The paper details the fundamental principles of this method, emphasizing the role of entanglement in achieving precise geometric representation and optimized parameter adjustment. We present a theoretical framework and initial experimental validation demonstrating the potential of this approach to address challenges in geometric modeling, particularly those involving intricate, dynamically evolving structures. The research explores the implications of entanglement in enhancing geometric fidelity and provides a foundation for future advancements in quantum-enhanced geometry.

Aug 31, 20260 citations