An Improved GAS Algorithm.

IF 2.1 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Entropy Pub Date : 2025-02-26 DOI:10.3390/e27030240

Zhijian Wang, Yuchen He, Tian Luan, Yong Long

引用次数: 0

Abstract

This paper introduces an improved Grover Adaptive Search (GAS) algorithm. The GAS algorithm has been prove to achieve quadratic acceleration in the Constrained Polynomial Binary Optimization (CPBO) problem. Nevertheless, the acceleration effect of the GAS algorithm can be decreased by the poor threshold selection. This article uses the Quantum Approximate Optimization Algorithm (QAOA) to improve the initial threshold selection, thereby accelerating the convergence speed of the original GAS algorithm. The acceleration effect of the improved GAS algorithm is presented by the Max-Cut problem and the CPBO problem.

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本文介绍了一种改进的格罗弗自适应搜索（GAS）算法。事实证明，GAS 算法能在受约束多项式二元优化（CPBO）问题中实现二次加速。然而，GAS 算法的加速效果会因阈值选择不当而降低。本文利用量子近似优化算法（QAOA）改进了初始阈值选择，从而加快了原始 GAS 算法的收敛速度。通过 Max-Cut 问题和 CPBO 问题展示了改进的 GAS 算法的加速效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Entropy PHYSICS, MULTIDISCIPLINARY-

CiteScore

4.90

自引率

11.10%

发文量

1580

审稿时长

21.05 days

期刊介绍： Entropy (ISSN 1099-4300), an international and interdisciplinary journal of entropy and information studies, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish as much as possible their theoretical and experimental details. There is no restriction on the length of the papers. If there are computation and the experiment, the details must be provided so that the results can be reproduced.