Quantum machine learning

• 来源期刊: 国家科学评论（英文版）
• 年，卷（期）: 2019,（1）
• 页码范围: 26-28
• 页数: 3页
• 语种: 英文
• DOI: 10.1093/nsr/nwy149

摘要

INTRODUCTION Quantum machine leaming 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 networks,an area that hopes to build on the enormous impact that classical neural networks have had.In particular,we concentrate on quantum generalizations of popular neural-network concepts such as Boltzmann machines,generative adversarial networks and autoencoders,as well as applications of classical neural networks to quantum information and vice versa.