高能物理中的量子机器学习事件分类
Event Classification with Quantum Machine Learning in High-Energy Physics
摘要：
我们目前的研究量子算法利用机器学习分类感兴趣的事件从背景事件，在高能物理中最具代表性的机器学习应用之一。我们专注于变分量子方法来学习输入数据的属性，并使用模拟器和量子计算设备来评估事件分类的性能。与基于提升决策树和使用经典计算机的深度神经网络的标准多变量分类技术的性能比较表明，量子算法在输入变量数量和训练样本大小的考虑范围内具有与标准技术相当的性能。在量子计算机上对变分量子算法进行了验证，证明了该算法在区分感兴趣事件和背景事件方面的可行性。讨论了在使用具有扩展门结构的量子线路的学习过程中观察到的特征行为，以及当前性能对高能物理实验中的应用的影响。
英文摘要：
We present studies of quantum algorithms exploiting machine learning to classify events of interest from background events, one of the most representative machine learning applications in high-energy physics. We focus on variational quantum approach to learn the properties of input data and evaluate the performance of the event classification using both simulators and quantum computing devices. Comparison of the performance with standard multi-variate classification techniques based on a boosted-decision tree and a deep neural network using classical computers shows that the quantum algorithm has comparable performance with the standard techniques at the considered ranges of the number of input variables and the size of training samples. The variational quantum algorithm is tested with quantum computers, demonstrating that the discrimination of interesting events from background is feasible. Characteristic behaviors observed during a learning process using quantum circuits with extended gate structures are discussed, as well as the implications of the current performance to the application in high-energy physics experiments.
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