Raubitzek Sebastian, Mallinger Kevin
Data Science Research Unit, TU Wien, Favoritenstrasse 9-11/194, 1040 Vienna, Austria.
SBA Research gGmbH, Floragasse 7/5.OG, 1040 Vienna, Austria.
Entropy (Basel). 2023 Jun 28;25(7):992. doi: 10.3390/e25070992.
In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the variational quantum circuit and the quantum kernel estimator (QKE). We provide a first evaluation on the performance of these classifiers when using a hyperparameter search on six widely known and publicly available benchmark datasets and analyze how their performance varies with the number of samples on two artificially generated test classification datasets. As quantum machine learning is based on unitary transformations, this paper explores data structures and application fields that could be particularly suitable for quantum advantages. Hereby, this paper introduces a novel dataset based on concepts from quantum mechanics using the exponential map of a Lie algebra. This dataset will be made publicly available and contributes a novel contribution to the empirical evaluation of quantum supremacy. We further compared the performance of VQC and QKE on six widely applicable datasets to contextualize our results. Our results demonstrate that the VQC and QKE perform better than basic machine learning algorithms, such as advanced linear regression models (Ridge and Lasso). They do not match the accuracy and runtime performance of sophisticated modern boosting classifiers such as XGBoost, LightGBM, or CatBoost. Therefore, we conclude that while quantum machine learning algorithms have the potential to surpass classical machine learning methods in the future, especially when physical quantum infrastructure becomes widely available, they currently lag behind classical approaches. Our investigations also show that classical machine learning approaches have superior performance classifying datasets based on group structures, compared to quantum approaches that particularly use unitary processes. Furthermore, our findings highlight the significant impact of different quantum simulators, feature maps, and quantum circuits on the performance of the employed quantum estimators. This observation emphasizes the need for researchers to provide detailed explanations of their hyperparameter choices for quantum machine learning algorithms, as this aspect is currently overlooked in many studies within the field. To facilitate further research in this area and ensure the transparency of our study, we have made the complete code available in a linked GitHub repository.
在本文中，我们使用来自Qiskit Python环境的两个量子分类器——变分量子电路和量子核估计器（QKE），研究量子机器学习在分类任务中的适用性。我们在六个广为人知且公开可用的基准数据集上进行超参数搜索时，对这些分类器的性能进行了首次评估，并分析了它们在两个人工生成的测试分类数据集上的性能如何随样本数量变化。由于量子机器学习基于酉变换，本文探索了可能特别适合量子优势的数据结构和应用领域。据此，本文使用李代数的指数映射引入了一个基于量子力学概念的新颖数据集。该数据集将公开提供，并为量子优越性的实证评估做出新贡献。我们还在六个广泛适用的数据集上比较了VQC和QKE的性能，以更好地理解我们的结果。我们的结果表明，VQC和QKE的性能优于基本机器学习算法，如高级线性回归模型（岭回归和套索回归）。它们无法与复杂的现代提升分类器（如XGBoost、LightGBM或CatBoost）的准确性和运行时性能相匹配。因此，我们得出结论，虽然量子机器学习算法未来有可能超越经典机器学习方法，特别是当物理量子基础设施广泛可用时，但它们目前仍落后于经典方法。我们的研究还表明，与特别使用酉过程的量子方法相比，经典机器学习方法在基于群结构的数据集分类方面具有卓越性能。此外，我们的发现突出了不同量子模拟器、特征映射和量子电路对所采用量子估计器性能的重大影响。这一观察结果强调了研究人员需要为量子机器学习算法的超参数选择提供详细解释，因为该领域目前许多研究都忽略了这一方面。为了促进该领域的进一步研究并确保我们研究的透明度，我们已将完整代码发布在链接的GitHub存储库中。
