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Distributed secure quantum machine learning
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DOI：10.1016/j.scib.2017.06.007.png)
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Abstract
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.
Keywords:
Quantum machine learning
Quantum communication
Quantum computation
Big data
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