Jinguang Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Jinguang Wang has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Gaofeng Luo
Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, School of Information Science and Engineering, Shaoyang University, Shaoyang 422000, China
1 shared publication - Hui Hu
School of Automation, Central South University, Changsha 410083, China
1 shared publication - Yijun Wang
School of Automation, Central South University, Changsha 410083, China
1 shared publication - Ying Guo
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
1 shared publication - Yiwu Zhu
School of Automation, Central South University, Changsha 410083, China
1 shared publication - Yun Mao
School of Information Engineering, Shaoyang University, Shaoyang 422000, China
1 shared publication
Latest Publications
- Open Access
Neural Network-Based Prediction for Secret Key Rate of Underwater Continuous-Variable Quantum Key Distribution through a Seawater Channel
Continuous-variable quantum key distribution (CVQKD) plays an important role in quantum communications, because of its compatible setup for optical implementation with low cost. For this paper, we considered a neural network approach to predicting the secret key rate of CVQKD with discrete modulation (DM) through an underwater channel. A long-short-term-memory-(LSTM)-based neural network (NN) model was employed, in order to demonstrate performance improvement when taking into account the secret key rate. The numerical simulations showed that the lower bound of the secret key rate could be achieved for a finite-size analysis, where the LSTM-based neural network (NN) was much better than that of the backward-propagation-(BP)-based neural network (NN). This approach helped to realize the fast derivation of the secret key rate of CVQKD through an underwater channel, indicating that it can be used for improving performance in practical quantum communications.
Open AccessNeural Network-Based Prediction for Secret Key Rate of Underwater Continuous-Variable Quantum Key Distribution through a Seawater Channel
Continuous-variable quantum key distribution (CVQKD) plays an important role in quantum communications, because of its compatible setup for optical implementation with low cost. For this paper, we considered a neural network approach to predicting the secret key rate of CVQKD with discrete modulation (DM) through an underwater channel. A long-short-term-memory-(LSTM)-based neural network (NN) model was employed, in order to demonstrate performance improvement when taking into account the secret key rate. The numerical simulations showed that the lower bound of the secret key rate could be achieved for a finite-size analysis, where the LSTM-based neural network (NN) was much better than that of the backward-propagation-(BP)-based neural network (NN). This approach helped to realize the fast derivation of the secret key rate of CVQKD through an underwater channel, indicating that it can be used for improving performance in practical quantum communications.