论文标题
高能量物理学中的量子机学习
Quantum Machine Learning in High Energy Physics
论文作者
论文摘要
机器学习长期以来一直在高能物理学中使用，主要是在分析级别和监督分类。量子计算是在1980年代初期假定的，是执行经典计算机无法处理的计算方式。随着嘈杂的中等规模量子计算设备的出现，正在开发更多的量子算法，目的是利用硬件用于机器学习应用程序的容量。一个有趣的问题是，是否有将量子机学习应用于高能量物理学的方法。本文回顾了第一代想法，这些想法在高能量物理学的问题上使用量子机学习，并为未来的应用提供前景。
Machine learning has been used in high energy physics for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable with a classical computer. With the advent of noisy intermediate-scale quantum computing devices, more quantum algorithms are being developed with the aim at exploiting the capacity of the hardware for machine learning applications. An interesting question is whether there are ways to apply quantum machine learning to High Energy Physics. This paper reviews the first generation of ideas that use quantum machine learning on problems in high energy physics and provide an outlook on future applications.
