基于量子纠缠的自适应算法 - 优化算法的量子计算基础

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper investigates the potential of leveraging quantum entanglement to revolutionize optimization algorithms. Traditional optimization methods often rely on classical computation, limiting their scalability and effectiveness in complex problems. We propose a novel algorithmic framework based on quantum entanglement, aiming to fundamentally alter the core computational process of optimization. The core claim is that this framework introduces a quantum-enhanced optimization mechanism, offering potential advantages in terms of speed, accuracy, and the ability to tackle previously intractable problems. We explore the theoretical implications and preliminary experimental results demonstrating the feasibility of this approach. This work represents a foundational step towards exploring the intersection of quantum computing and optimization, potentially unlocking new avenues in computational science and engineering.

DOI: 10.5281/zenodo.22219601

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