Here we will give a perspective on new possible interplays between Machine Learning and Quantum Physics, including also practical cases and applications. We will explore the ways in which machine learning could benefit from new quantum technologies and algorithms to find new ways to speed up their computations by breakthroughs in physical hardware, as well as to improve existing models or devise new learning schemes in the quantum domain. Moreover, there are lots of experiments in quantum physics that do generate incredible amounts of data and machine learning would be a great tool to analyze those and make predictions, or even control the experiment itself. On top of that, data visualization techniques and other schemes borrowed from machine learning can be of great use to theoreticians to have better intuition on the structure of complex manifolds or to make predictions on theoretical models. This new research field, named as Quantum Machine Learning, is very rapidly growing since it is expected to provide huge advantages over its classical counterpart and deeper investigations are timely needed since they can be already tested on the already commercially available quantum machines.

翻译：在这里,我们将对机器学习和量子物理之间可能的新相互作用,包括实际案例和应用进行审视。 我们将探讨机器学习如何从新的量子技术和算法中受益,以寻找新方法,通过物理硬件突破加快计算速度,以及改进现有模型或设计量子领域的新的学习计划。 此外,在量子物理方面有许多实验确实产生惊人的数据量,而机器学习将是分析这些数据和作出预测,甚至控制实验本身的伟大工具。 此外,数据可视化技术和其他从机器学习中借用的计划对于理论学家来说非常有用,可以对复杂的元体结构有更好的直觉,也可以对理论模型作出预测。 这个称为量子机器学习的新研究领域正在迅速发展,因为预期它能提供优于其古典的对等技术的巨大优势,并且需要更深入的调查是及时的,因为它们已经可以在商业上已有的量子机器上进行测试了。