Quantum machine learning

被引：0

作者：

Jonathan Allcock ^{[1
]}

Shengyu Zhang ^{[1
]}

机构：

[1] Tencent Quantum Laboratory

来源：

National Science Review | 2019年 / 6卷 / 01期

关键词：

RBM; Quantum machine learning;

D O I：

暂无

中图分类号：

TP181 [自动推理、机器学习]; O413 [量子论];

学科分类号：

070201 ; 081104 ; 0812 ; 0835 ; 1405 ;

摘要：

INTRODUCTION Quantum machine learning is at the crossroads of two of the most exciting current areas of research:quantum computing and classical machine learning. Although the field is still in its infancy,the body of literature is already large enough to warrant several review articles [1–3]. This short survey focuses on a selection of significant recent results on the subtopic of quantum neural net-

引用

页码：26 / 28

页数：3

共 50 条

[1] Quantum machine learning
[J]. Wu, Re-Bing (rbwu@tsinghua.edu.cn), 2017, South China University of Technology (34):
[2] Quantum machine learning
Allcock, Jonathan
Zhang, Shengyu
[J]. NATIONAL SCIENCE REVIEW, 2019, 6 (01) : 26 - +
[3] Quantum machine learning
von Lilienfeld, O. Anatole
[J]. ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2017, 253
[4] Quantum machine learning
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[J]. NATURE, 2017, 549 (7671) : 195 - 202
[5] Quantum machine learning
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[J]. Nature, 2017, 549 : 195 - 202
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[J]. ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2018, 256
[7] Quantum machine learning
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[J]. ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2018, 255
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[9] Quantum Machine Learning with SQUID
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[10] Federated Quantum Machine Learning
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[J]. ENTROPY, 2021, 23 (04)

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