将kNN算法的量子版本与基于振幅编码特征的度量进行基准测试
Areli-Yesareth Guerrero-Estrada1, L F Quezada2, Guo-Hua Sun1
1Computing Research Center, National Polytechnic Institute, 07700, Mexico City, Mexico.
Scientific reports
|July 19, 2024
概括
一个新的量子子程序显著降低了量子kNN算法对量子比特的要求. 这种节省内存的方法保持了性能,为量子机器学习应用提供了实际的进步.
科学领域:
- 量子计算是一种量子计算.
- 机器学习 机器学习
- 模式识别 模式识别
背景情况:
- k-最近邻居 (kNN) 算法是一种基本的机器学习技术.
- 量子计算为复杂的计算任务提供了潜在的加速度.
- 现有的量子kNN (QkNN) 算法面临着量子比特效率和内存要求的挑战.
研究的目的:
- 为模式距离计算引入一种新的,记忆效率高的量子子程序.
- 将这个子程序集成到两个著名的QkNN算法 (Schuld等人. 和 奎扎达等人. ) 的情况.
- 为了比较 QkNN 算法的性能和资源利用,使用和不使用拟议的子程序.
主要方法:
- 使用振幅编码特征计算模式距离的量子子程序的开发.
- 将子程序集成到 Schuld 和 Quezada 的 QkNN 框架中.
- 在13个不同的数据集中进行实证评估,比较量子比特的使用和分类指标 (准确性,F1得分).
主要成果:
- 拟议的子程序始终在两种QkNN算法中将所需的量子比特数量减少至少50%.
- 总体表现在很大程度上保持不变,一些数据集显示出改进,而另一些数据集在准确性或F1得分方面略有下降.
- 具体的改进在数据集中被注意到,例如对Schuld的算法进行冷疗法和平衡量级,以及对Quezada的算法进行剖腹产,哈伯曼和免疫疗法.
结论:
- 开发的量子子程序为QkNN算法提供了量子位效率的显著改善.
- 这种节省内存的方法是对实际量子机器学习实现的宝贵贡献.
- 进一步的研究可以探索这个量子距离计算方法的更广泛的应用和优化.
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