盗算法是由经典随机步行和量子步行驱动的
Tomoki Yamagami1, Etsuo Segawa2, Takatomo Mihana1
1Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-8656, Japan.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
量子步行提供了独特的扩散和定位特性,通过更好地平衡探索和利用,在多武器强盗问题中表现优于经典的随机步行.
科学领域:
- 量子计算和算法 量子计算和算法
- 理论计算机科学理论计算机科学
- 机器学习 机器学习
背景情况:
- 经典的随机步行 (RWs) 呈现线性扩散,但缺乏局部化.
- 量子步行 (QWs) 独特地结合了线性扩散和定位.
- 多武装强盗 (MAB) 问题涉及到平衡勘探和开发.
研究的目的:
- 用RW和QW框架开发MAB问题的新算法.
- 调查基于QW的方法对MAB的基于RW的方法的性能优势.
- 为了提高MAB战略,利用QW的独特特性.
主要方法:
- 为MAB开发基于RW的算法.
- 为MAB开发基于QW的算法.
- 在各种设置下对算法性能进行比较分析.
主要成果:
- 在特定的MAB场景中,基于QW的算法与基于RW的算法相比,表现优越.
- 增强的性能归因于QW能够有效管理勘探和开采的能力.
- QWs的定位和扩散特性是它们MAB优势的关键.
结论:
- 量子步行为解决复杂的MAB问题提供了一个强大的框架.
- 基于QW的策略为优化不确定性下决策提供了一个有希望的替代方案.
- 这项研究突出了量子步行特性在机器学习中的实际应用.
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