基于量子纠缠的几何变形优化算法

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel geometric optimization algorithm based on quantum entanglement, aiming to enhance the precision of numerical solutions through the manipulation of geometric configurations. The algorithm leverages the unique properties of quantum entanglement to achieve a fundamentally different approach to optimization compared to conventional methods. We explore the potential of this approach to address challenges in geometric modeling and design, particularly when dealing with complex shapes and intricate structural relationships. The core mechanism involves encoding geometric parameters as entangled states, allowing for a non-local manipulation of the solution space. We present a theoretical framework for this algorithm and demonstrate its efficacy through a series of numerical simulations, showcasing significant improvements in solution accuracy and efficiency. This research offers a promising avenue for advancing geometric optimization techniques, potentially impacting diverse fields such as computational design and structural analysis.

DOI: 10.5281/zenodo.22196966

Related papers

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

Quantum Entanglement-Driven Geometric Optimization – Algorithm Design

Jincheng Zhang

Quantum entanglement offers a unique paradigm for computational exploration, potentially enabling breakthroughs in complex optimization problems. This paper explores the design of a novel geometric optimization algorithm leveraging the inherent properties of quantum entanglement to achieve enhanced precision and efficiency. We propose a new approach that dynamically adjusts geometric parameters using entangled qubit states, allowing for a fundamentally different optimization process compared to classical methods. The core mechanism centers around the exploitation of entanglement to create a parallel exploration of the solution space, resulting in a more robust and adaptable optimization strategy. The research investigates the potential of this algorithm to tackle challenging geometric optimization tasks, specifically focusing on the design of a self-adaptive system for parameter tuning within complex shapes. The paper concludes with a discussion of the theoretical implications and preliminary experimental results demonstrating the algorithm's efficacy.

Aug 31, 20260 citations

Based Quantum Entanglement Optimization

Jincheng Zhang

This paper investigates the application of quantum entanglement to optimize geometric shapes. We propose a novel optimization algorithm, leveraging the unique properties of quantum entanglement, to achieve more efficient geometric solutions. The core mechanism involves encoding geometric configurations within a quantum state, enabling a fundamentally different approach to exploring the search space. The algorithm's potential lies in its ability to bypass local optima, a significant challenge in traditional optimization techniques. This research explores the feasibility of utilizing entanglement to identify and refine geometric structures, offering a promising pathway towards enhanced optimization efficiency.

Sep 3, 20260 citations

拓扑优化算法的量子化驱动

Jincheng Zhang

This paper explores the application of quantum-enhanced optimization algorithms to complex geometric structures, specifically focusing on topology optimization. Traditional optimization methods often struggle with intricate geometries, leading to computational bottlenecks and suboptimal solutions. We propose a novel approach that leverages quantum entanglement to encode and manipulate topological structures, enabling a fundamentally different optimization process. This research investigates the potential of quantum-driven optimization to address challenges in fields like engineering design, materials science, and computational geometry. The core of our work centers on a quantum-based representation of topological properties and the utilization of entangled quantum states to guide the optimization process towards efficient solutions. We detail the proposed quantum mechanism, the mathematical foundations underpinning the algorithm, and preliminary experimental results demonstrating improved performance compared to classical optimization techniques in specific geometric datasets. This work contributes to the advancement of quantum-enhanced optimization, offering a pathway towards solving complex problems with enhanced efficiency and robustness.

Sep 3, 20260 citations

基于量子纠缠的矩阵变换的几何优化

Jincheng Zhang

This paper presents a novel geometric optimization algorithm based on quantum entanglement, leveraging the unique properties of entangled states to achieve a more intelligent optimization process. We introduce a 'quantum entanglement degree' metric to quantify the entanglement strength within the optimization landscape, allowing for targeted adjustments to steer the optimization towards desired solutions. Traditional matrix factorization methods struggle with complex, high-dimensional spaces, often requiring extensive parameter tuning. Our proposed algorithm offers a more robust and adaptable approach, potentially surpassing the capabilities of existing methods, particularly in scenarios involving intricate geometric constraints. The algorithm's effectiveness is demonstrated through a series of numerical examples, showcasing improved convergence and solution quality compared to established optimization techniques. The core mechanism centers around utilizing entanglement to identify and manipulate the solution space in a more nuanced manner.

Sep 7, 20260 citations

基于量子算法的几何优化设计

Jincheng Zhang

This paper introduces a novel geometric optimization approach leveraging quantum algorithms to address complex geometric shape parameter optimization. Current methods often rely on manual design, limiting scalability and efficiency. We propose a quantum-enhanced optimization strategy that utilizes quantum algorithms to identify optimal parameter configurations, exhibiting significant performance advantages over classical approaches. The core mechanism focuses on employing quantum properties to explore the solution space more efficiently, ultimately enabling the design of intricate geometric shapes with enhanced precision and speed. The proposed method demonstrates promising results in a benchmark set of geometric optimization problems, showcasing the potential of quantum computing for accelerating geometric design processes.

Sep 6, 20260 citations