论文标题
将撞机装在量子计算机中：应对大数据集的量子机学习的挑战
Fitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets
论文作者
论文摘要
当前的量子系统具有严重的局限性，影响具有高维度的大型数据集的处理，这是高能量物理的典型代理。在本文中，研究了功能和数据原型选择技术以应对这一挑战。进行了网格搜索，并根据经典的浅机学习方法对量子机学习模型进行了训练和基准测试，并在减少和完整的数据集中培训。即使使用大数据集，量子算法的性能也与经典的性能相当。顺序的向后选择和主成分分析技术用于特征的选择，而前者可以在特定情况下产生更好的量子机学习模型，但它更不稳定。此外，我们表明结果中的这种可变性是由使用离散变量的使用引起的，突出了主成分分析的适用性转换了高能量物理环境中量子机学习应用的数据。
Current quantum systems have significant limitations affecting the processing of large datasets with high dimensionality, typical of high energy physics. In the present paper, feature and data prototype selection techniques were studied to tackle this challenge. A grid search was performed and quantum machine learning models were trained and benchmarked against classical shallow machine learning methods, trained both in the reduced and the complete datasets. The performance of the quantum algorithms was found to be comparable to the classical ones, even when using large datasets. Sequential Backward Selection and Principal Component Analysis techniques were used for feature's selection and while the former can produce the better quantum machine learning models in specific cases, it is more unstable. Additionally, we show that such variability in the results is caused by the use of discrete variables, highlighting the suitability of Principal Component analysis transformed data for quantum machine learning applications in the high energy physics context.
