Variational learning the SDC quantum protocol with gradient-based optimization

被引：0
|
作者：
Haozhen Situ
Zhiming Huang
Xiangfu Zou
Shenggen Zheng
机构：
[1] South China Agricultural University,College of Mathematics and Informatics
[2] Peng Cheng Laboratory,Center for Quantum Computing
[3] Wuyi University,School of Economics and Management
[4] Southern University of Science and Technology,Institute for Quantum Science and Engineering
[5] Wuyi University,School of Mathematics and Computational Science
来源：
Quantum Information Processing | 2019年 / 18卷
关键词：
Quantum machine learning; Quantum protocol; Simultaneous dense coding;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Recently, a variational learning approach is adopted to discover quantum communication protocols (Wan et al. in npj Quantum Inf 3:36, 2017). Because designing quantum protocols manually is a delicate and difficult work, this variational learning approach is well worth further study. In this paper, we use the same approach to learn the simultaneous dense coding (SDC) protocols with two or three receivers. The gradient-based optimization is used to learn the parameters of the locking operator of the SDC protocol. Two different designs of the loss function are considered. Numerical experiment results show the effectiveness of this variational learning approach.
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