近些年来，机器学习对各行各业产生了深远影响，特别是把量子计算的特性与机器学习相结合而形成的量子机器学习，实现了对传统算法的加速. 目前，量子机器学习在物理、化学、金融和生物医药等领域的应用引起了人们的极大关注. 本文首先介绍了量子机器学习的基本概念和目前的前沿进展. 其次以氟化氢分子为例子，利用量子机器学习计算了该分子系统的基态能量. 

In the past few decades, machine learning showed a profound impact on all kinds of industries. In recent years, researchers have combined quantum computing with machine learning, and proposed quantum machine learning to realize the acceleration of traditional algorithms. At present, the applications of quantum machine learning in physics, chemistry, finance, biomedicine and other fields have attracted great attention. This paper first introduces the basic concepts of quantum machine learning and the current advances. Secondly, taking hydrogen fluoride（HF） molecule as an example, we discuss the application of quantum machine learning in the calculation of ground state energy of physical systems and also demonstrate the role of quantum machine learning.