Quantum Machine Learning: Current State and Challenges

被引:3
作者
Avramouli, Maria [1 ]
Savvas, Ilias K. [1 ]
Garani, Georgia [1 ]
Vasilaki, Anna [2 ]
机构
[1] Univ Thessaly, Dept Digital Syst, Larisa, Greece
[2] Univ Thessaly, Fac Med, Sch Hlth Sci, Lab Pharmacol, Larisa, Greece
来源
25TH PAN-HELLENIC CONFERENCE ON INFORMATICS WITH INTERNATIONAL PARTICIPATION (PCI2021) | 2021年
关键词
Quantum computing; Quantum machine learning;
D O I
10.1145/3503823.3503896
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In recent years, machine learning has penetrated a large part of our daily lives, which creates special challenges and impressive progress in this area. Nevertheless, as the amount of daily data is grown, learning time is increased. Quantum machine learning (QML) may speed up the processing of information and provide great promise in machine learning. However, it is not used in practice yet, because quantum software and hardware challenges are still unsurmountable. This paper provides current research of quantum computing and quantum machine learning algorithms. Also, the quantum vendors, their frameworks, and their platforms are presented. A few fully implemented versions of quantum machine learning are presented, which are easier to be evaluated. Finally, QML's challenges, and problems are discussed.
引用
收藏
页码:397 / 402
页数:6
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