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Parameter Optimization Based BPNN of Atmosphere Continuous-Variable Quantum Key Distribution

1
School of Computer Science and Engineering, Central South University, Changsha 410083, China
2
School of Automation, Central South University, Changsha 410083, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(9), 908; https://doi.org/10.3390/e21090908
Received: 11 August 2019 / Revised: 12 September 2019 / Accepted: 16 September 2019 / Published: 18 September 2019
(This article belongs to the Special Issue Quantum Information Processing)
The goal of continuous variable quantum key distribution (CVQKD) is to be diffusely used and adopted in diverse scenarios, so the adhibition of atmospheric channel will play a crucial part in constituting global secure quantum communications. Atmospheric channel transmittance is affected by many factors and does not vary linearly, leading to great changes in signal-to-noise ratio. It is crucial to choose the appropriate modulation variance under different turbulence intensities to acquire the optimal secret key rate. In this paper, the four-state protocol, back-propagation neural network (BPNN) algorithm was discussed in the proposed scheme. We employ BPNN to CVQKD, which could adjust the modulation variance to an optimum value for ensuring the system security and making the system performance optimal. The numerical results show that the proposed scheme is equipped to improve the secret key rate efficiently. View Full-Text
Keywords: continuous variable quantum key distribution; atmosphere turbulence; machine learning; back-propagation neural network continuous variable quantum key distribution; atmosphere turbulence; machine learning; back-propagation neural network
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Su, Y.; Guo, Y.; Huang, D. Parameter Optimization Based BPNN of Atmosphere Continuous-Variable Quantum Key Distribution. Entropy 2019, 21, 908.

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