基于自旋体系的量子机器学习实验进展  

Experimental progress of quantum machine learning based on spin systems

作　　者：田宇 林子栋 王翔宇 车良宇 鲁大为 Tian Yu;Lin Zi-Dong;Wang Xiang-Yu;Che Liang-Yu;Lu Da-Wei(Department of Physics,Southern University of Science and Technology,Shenzhen 518055,China)

机构地区：[1]南方科技大学物理系,深圳518055

出　　处：《物理学报》2021年第14期62-75,共14页Acta Physica Sinica

基　　金：国家重点研究发展计划(批准号:2019YFA0308100);国家自然科学基金(批准号:12075110,11975117,11905099,11875159,U1801661);广东基础和应用基础研究基金会(批准号:2019A1515011383);广东省国际合作计划(批准号:2020A0505100001);深圳市科学技术和创新委员会(批准号:ZDSYS20170303165926217,KQTD20190929173815000,JCYJ20200109140803865,JCYJ20170412152620376,JCYJ20180302174036418);鹏城学者、广东省创新研究与计划中心(批准号:2019ZT08C044);广东省重点实验室(批准号:2019B121203002)资助的课题.

摘　　要：机器学习因其在模式识别等问题上的优势已经被广泛应用到各个研究领域,然而其运算能力在一定程度上受到经典计算机算力的制约.近年来,随着量子技术的高速发展,量子计算加速的机器学习在诸多量子体系中进行了初步实验验证,并在某些特定问题上展示出了超越经典算法的优势.本文主要介绍两类典型的自旋体系-核磁共振体系和金刚石氮空位色心体系,并回顾近年来量子机器学习在这两类体系上的一些代表性实验工作.Machine learning is widely applied in various areas due to its advantages in pattern recognition,but it is severely restricted by the computing power of classic computers.In recent years,with the rapid development of quantum technology,quantum machine learning has been verified experimentally verified in many quantum systems,and exhibited great advantages over classical algorithms for certain specific problems.In the present review,we mainly introduce two typical spin systems,nuclear magnetic resonance and nitrogen-vacancy centers in diamond,and review some representative experiments in the field of quantum machine learning,which were carried out in recent years.

关 键 词：量子机器学习 自旋体系 核磁共振 氮空位色心 

分 类 号：O413[理学—理论物理] TP181[理学—物理]