Neural network-based prediction of the secret-key rate of quantum key
distribution
- URL: http://arxiv.org/abs/2108.02578v3
- Date: Mon, 30 May 2022 01:50:13 GMT
- Title: Neural network-based prediction of the secret-key rate of quantum key
distribution
- Authors: Min-Gang Zhou, Zhi-Ping Liu, Wen-Bo Liu, Chen-Long Li, Jun-Lin Bai,
Yi-Ran Xue, Yao Fu, Hua-Lei Yin, Zeng-Bing Chen
- Abstract summary: We construct a neural network that can quickly predict the secure key rate based on the experimental parameters and experimental results.
This allows the secure key rate of discrete-modulated CV-QKD to be extracted in real time on a low-power platform.
- Score: 6.040436695129137
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Numerical methods are widely used to calculate the secure key rate of many
quantum key distribution protocols in practice, but they consume many computing
resources and are too time-consuming. In this work, we take the homodyne
detection discrete-modulated continuous-variable quantum key distribution
(CV-QKD) as an example, and construct a neural network that can quickly predict
the secure key rate based on the experimental parameters and experimental
results. Compared to traditional numerical methods, the speed of the neural
network is improved by several orders of magnitude. Importantly, the predicted
key rates are not only highly accurate but also highly likely to be secure.
This allows the secure key rate of discrete-modulated CV-QKD to be extracted in
real time on a low-power platform. Furthermore, our method is versatile and can
be extended to quickly calculate the complex secure key rates of various other
unstructured quantum key distribution protocols.
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