基于量子算法的几何优化设计

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

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.

DOI: 10.5281/zenodo.22471124

Related papers

基于量子纠缠的几何变形优化算法

Jincheng Zhang

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.

Aug 31, 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

基于量子纠缠的动态几何优化算法

Jincheng Zhang

This paper presents a novel dynamic geometric optimization algorithm based on the principles of quantum entanglement. Traditional geometric optimization methods often rely on manual parameter tuning, limiting efficiency and requiring significant human intervention. This research introduces a new algorithm that leverages quantum entanglement to dynamically adjust geometric transformations, resulting in enhanced optimization performance. The core mechanism involves creating entangled states to represent geometric parameters, enabling instantaneous adjustments and accelerating the optimization process. We detail the algorithm's design, implementation, and preliminary results, demonstrating significant improvements in optimization speed and quality compared to traditional methods. The paper concludes with a discussion of the potential implications of this approach for a wide range of geometric optimization problems.

Sep 1, 20260 citations

基于量子态的自适应几何优化

Jincheng Zhang

This paper introduces a novel approach to geometric optimization leveraging the inherent quantum state properties of the problem. Traditional geometric optimization methods often require manual parameter tuning, limiting precision and requiring significant computational effort. We propose a quantum-inspired algorithm that utilizes quantum state transformations to dynamically adjust parameters during the optimization process, leading to improved geometric accuracy and robustness. The core mechanism involves mapping the problem's objective function to a quantum state space, allowing for efficient exploration of the solution space. We demonstrate the efficacy of this method through a series of numerical experiments, showcasing significant improvements in geometric accuracy compared to standard optimization techniques. The algorithm's ability to adapt to the problem's characteristics makes it a promising tool for complex geometric optimization challenges.

Aug 31, 20260 citations

Dynamic Topology Optimization Based on Quantum Computing Geometric Model

Jincheng Zhang

This paper presents a novel approach to dynamic topology optimization, leveraging quantum computing to accelerate the process of generating and refining geometric models. Traditional topology optimization relies heavily on computationally intensive simulations, often requiring significant time and resources. We propose a quantum-enhanced algorithm that utilizes quantum algorithms to efficiently explore the geometric space, thereby reducing the computational burden and enabling the discovery of optimal designs. The core mechanism focuses on utilizing quantum state representations to represent and manipulate geometric parameters, facilitating faster convergence and more nuanced optimization. The study demonstrates the potential for significant improvements in optimization speed and accuracy compared to classical methods, particularly for complex geometries. The research highlights the promise of quantum computing to revolutionize geometric design and problem-solving.

Sep 6, 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