关于量子机器学习的适用性
Sebastian Raubitzek1,2, Kevin Mallinger1,2
1Data Science Research Unit, TU Wien, Favoritenstrasse 9-11/194, 1040 Vienna, Austria.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
量子机器学习分类器,如变量量子电路和量子内核估计器,显示出有希望的结果,但目前的性能低于先进的经典方法. 需要进一步的研究来优化量子方法以获得更广泛的适用性和量子优势.
科学领域:
- 量子计算是一种量子计算.
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 量子机器学习 (QML) 为增强的计算任务提供了潜力.
- 经典的机器学习算法是高度优化和广泛使用的.
研究的目的:
- 评估量子分类器 (变量量子电路和量子内核估计器) 在分类任务中的性能.
- 将QML性能与基准和新型数据集上的经典算法进行比较.
- 探索适合量子优势的数据结构.
主要方法:
- 使用 Qiskit.it 实现并评估了变量量子电路 (VQC) 和量子内核估计器 (QKE).
- 在六个基准数据集上进行超参数搜索,并在人工数据集上分析不同样本大小的性能.
- 介绍了一个基于量子力学概念的新型数据集.
主要成果:
- VQC和QKE的表现优于基本线性模型,但在准确性和运行时间方面落后于先进的经典分类器 (XGBoost,LightGBM,CatBoost).
- 经典方法在使用组结构的数据集上表现出优异的性能,与使用单元过程的量子方法相比.
- 量子模拟器,特征图和电路选择显著影响QML估计器性能.
结论:
- QML算法显示了未来的潜力,但目前无法与最先进的古典方法相匹配.
- 经典机器学习优于群组结构化数据,而不是当前的量子方法.
- 在QML超参数选择中的透明度对于可重现性和进步至关重要.
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