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Neural Network-Based Prediction for Secret Key Rate of Underwater Continuous-Variable Quantum Key Distribution

Yun Mao1,2, Yiwu Zhu2, Hui Hu2

  • 1School of Information Engineering, Shaoyang University, Shaoyang 422000, China.

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This study introduces a neural network approach for predicting the secret key rate in continuous-variable quantum key distribution (CVQKD) over underwater channels. The LSTM-based neural network significantly improves performance compared to traditional methods.

Keywords:
continuous-variableneural networkquantum key distributionunderwater channel

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Area of Science:

  • Quantum Communications
  • Applied Physics
  • Machine Learning

Background:

  • Continuous-variable quantum key distribution (CVQKD) is crucial for secure communication due to its cost-effective optical implementation.
  • Underwater channels present unique challenges for quantum communication systems.
  • Predicting the secret key rate is essential for assessing the performance and security of CVQKD.

Purpose of the Study:

  • To investigate a neural network approach for predicting the secret key rate of discrete modulation CVQKD through underwater channels.
  • To evaluate the performance of a Long Short-Term Memory (LSTM)-based neural network for this task.
  • To demonstrate the potential for improving practical quantum communication systems.

Main Methods:

  • Utilized a Long Short-Term Memory (LSTM)-based neural network (NN) model.
  • Simulated continuous-variable quantum key distribution (CVQKD) with discrete modulation (DM) through an underwater channel.
  • Compared the LSTM-based NN performance against a backward-propagation (BP)-based NN for secret key rate prediction.

Main Results:

  • The LSTM-based neural network achieved a lower bound for the secret key rate in finite-size analysis.
  • The LSTM-based NN demonstrated significantly better performance than the BP-based NN.
  • The approach enabled fast derivation of the secret key rate for underwater CVQKD.

Conclusions:

  • The LSTM-based neural network is a highly effective method for predicting the secret key rate of CVQKD in underwater environments.
  • This method offers a significant performance improvement over traditional neural network models.
  • The findings support the application of this approach for enhancing practical quantum communication systems.