主成分分析和t分布式随机邻居嵌入分析在量子近似优化算法纠和非纠混合运算符的研究中
Brian García Sarmina1, Guo-Hua Sun1, Shi-Hai Dong1,2
1Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City 07738, Mexico.
Entropy (Basel, Switzerland)
|November 24, 2023
概括
在量子近似优化算法 (QAOA) 中纠的混合运算符更好地保存信息,并显示更强的相关性. 这项研究使用PCA和t-SNE分析QAOA参数,揭示了纠和非纠模型之间的明显区别.
科学领域:
- 量子计算是一种量子计算.
- 机器学习 机器学习
- 数据分析 数据分析
背景情况:
- 量子近似优化算法 (QAOA) 是解决优化问题的著名算法.
- 了解混合运营商的作用,特别是纠的运营商,对于提高QAOA绩效至关重要.
- 像PCA和t-SNE这样的维度减小技术为分析复杂量子系统行为提供了强大的工具.
研究的目的:
- 在QAOA中调查和比较纠和非纠混合操作员的行为.
- 分析QAOA深度对这些运营商的性能和信息保存能力的影响.
- 利用PCA和t-SNE来更深入地了解参数景观和模型区别.
主要方法:
- 利用主要组件分析 (PCA) 和t分布式静态邻居嵌入 (t-SNE) 来进行数据可视化和分析.
- 采用了一组针对QAOA优化参数的数据集,应用于最大切割问题 (循环和完整配置).
- 在各种深度 (1L,2L,3L) 分析QAOA模型,在混合操作员中有或没有纠,检查RZ,RX和RY参数.
主要成果:
- PCA和t-SNE分析显示了纠与非纠QAOA模型的独特模式.
- 纠的QAOA模型通常在其参数映射中表现出优越的信息保存.
- 在解释变异 (PCA) 和库尔巴克-莱布勒分歧 (t-SNE) 中观察到可量化的差异,纠的模型显示了更大的信息相关性和聚类.
结论:
- 纠的混合运营商在QAOA中的信息保存和相关性方面提供了优势.
- PCA和t-SNE有效地区分了纠和非纠的QAOA模型的行为.
- 这些发现表明,将纠纳入QAOA混合运算符可以导致更强大的和信息化的参数空间.
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