.png){kind=icon}
返回
Distributed secure quantum machine learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.scib.2017.06.007.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Distributed secure quantum machine learning (DSQML) enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server with the privacy data preserved. Moreover, DSQML can be extended to a more general case that the client does not have enough data, and resorts both the remote quantum server and remote databases to perform the secure machine learning. Here we propose a DSQML protocol that the client can classify two-dimensional vectors to different clusters, resorting to a remote small-scale photon quantum computation processor. The protocol is secure without leaking any relevant information to the Eve. Any eavesdropper who attempts to intercept and disturb the learning process can be noticed. In principle, this protocol can be used to classify high dimensional vectors and may provide a new viewpoint and application for future big data. (C) 2017 Science China Press. Published by Elsevier B.V. and Science China Press. All rights reserved.
Keyword:
Quantum machine learning
Quantum communication
Quantum computation
Big data
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
21.1
论文数:
5.0K
被引数:
2.2W
机构
暂无机构信息
引用论文
分享收藏Quantum superposition, entanglement, and state teleportation of a microorganism on an electromechanical oscillator机电振荡器上微生物的量子叠加，纠缠和状态隐形传态.png)
SCIENCE BULLETIN
OAAI
SCIENCE BULLETINIF21.1
OAAI分享收藏分享收藏One-step hyperentanglement purification and hyperdistillation with linear opticsOPTICS EXPRESSOAAI
OPTICS EXPRESSIF3.3
OAAI分享收藏
查看更多内容