期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Quantum federated learning through blind quantum computing 被引量:1
1
作者 Weikang Li Sirui Lu Dong-Ling Deng 《Science China(Physics,Mechanics & Astronomy)》 SCIE EI CAS CSCD 2021年第10期64-71,共8页
Private distributed learning studies the problem of how multiple distributed entities collaboratively train a shared deep network with their private data unrevealed. With the security provided by the protocols of blin... Private distributed learning studies the problem of how multiple distributed entities collaboratively train a shared deep network with their private data unrevealed. With the security provided by the protocols of blind quantum computation, the cooperation between quantum physics and machine learning may lead to unparalleled prospect for solving private distributed learning tasks.In this paper, we introduce a quantum protocol for distributed learning that is able to utilize the computational power of the remote quantum servers while keeping the private data safe. For concreteness, we first introduce a protocol for private single-party delegated training of variational quantum classifiers based on blind quantum computing and then extend this protocol to multiparty private distributed learning incorporated with diferential privacy. We carry out extensive numerical simulations with diferent real-life datasets and encoding strategies to benchmark the efectiveness of our protocol. We find that our protocol is robust to experimental imperfections and is secure under the gradient attack after the incorporation of diferential privacy. Our results show the potential for handling computationally expensive distributed learning tasks with privacy guarantees, thus providing a valuable guide for exploring quantum advantages from the security perspective in the field of machine learning with real-life applications. 展开更多
关键词 quantum federated learning blind quantum computing diferential privacy quantum classifier
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部