大学物理 ›› 2023, Vol. 42 ›› Issue (8): 27-.doi: 10.16854/j.cnki.1000-0712.220509

• 基础物理教学现代化问题 • 上一篇    下一篇

量子机器学习简介及其在特定场景中的应用

朱钦圣,杨世璐,刘恒宇,滕保华   

  1. 电子科技大学物理学院,四川 成都611731
  • 收稿日期:2022-10-13 修回日期:2023-01-11 出版日期:2023-08-28 发布日期:2023-09-01
  • 作者简介:朱钦圣(1978—),男,四川成都人,电子科技大学物理学院副教授,博士,主要从事大学物理教学和量子机器学习及应用研究工作.E-mail:zhuqinsheng@uestc.edu.cn
  • 基金资助:
    2021 年高等学校教学研究项目(DWJZW202139xn),成都市技术创新研发项目(2021-YF05-02413-GX )资助

Introduction of quantummachine learning and its application in a certain scenario

ZHU Qin-sheng, YANG Shi-lu, LIU Heng-yu, TENG Bao-hua   

  1. School of Physics, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
  • Received:2022-10-13 Revised:2023-01-11 Online:2023-08-28 Published:2023-09-01

摘要: 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. 

关键词: 量子机器学习, 量子计算, 计算物理, 人工智能

Abstract: 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.

Key words: quantum machine learning, quantum computing, computational physics, artificial intelligence