Neural network method: withstanding noise for continuous-variable quantum key distribution with discrete modulation

Dingmin Cheng; Yewei Guo; Jiayang Dai; Hao Wu; Ying Guo

doi:10.1364/JOSAB.509184

Journal of the Optical Society of America B
Vol. 41,
Issue 4,
pp. 879-886
(2024)
•https://doi.org/10.1364/JOSAB.509184

Neural network method: withstanding noise for continuous-variable quantum key distribution with discrete modulation

Dingmin Cheng, Yewei Guo, Jiayang Dai, Hao Wu, and Ying Guo

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Dingmin Cheng, Yewei Guo, Jiayang Dai, Hao Wu, and Ying Guo, "Neural network method: withstanding noise for continuous-variable quantum key distribution with discrete modulation," J. Opt. Soc. Am. B 41, 879-886 (2024)

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Table of Contents Category
- Quantum Optics
Optics & Photonics Topics
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The topics in this list come from the Optics and Photonics Topics applied to this article.

About this Article
History
- Original Manuscript: October 18, 2023
- Revised Manuscript: February 19, 2024
- Manuscript Accepted: February 20, 2024
- Published: March 6, 2024

Abstract

Excess noise in continuous-variable quantum key distribution systems usually results in a loss of key rate, leading to fatal security breaches. This paper proposes a long short-term memory time-sequence neural network to predict the key rate of the system while counteracting the effects of excess noise. The proposed network model, which can be updated with historical data, predicts the key rate of the future moment for the input time-sequence data. To increase the key rate, we perform a postselection operation to combat excess noise. We demonstrate the asymptotic security of the protocol against collective attacks with the numerical simulations using the quadrature phase-shift keying protocol, where some parameters have been optimized to resist excess noise. It provides a potential solution for improving the security of quantum communication in practical applications.

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Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Excess Noise	Distance (km)	Improvement
0.01	50	18.2%
0.02	50	30.7%
0.03	25	31.4%
0.04	45	56.3%

Distance	Numerical Method (s)	LSTM (s)
10	$1.743 \times 10^{3}$	$1.753 \times 10^{- 4}$
20	$1.446 \times 10^{3}$	$7.757 \times 10^{- 5}$
30	$1.025 \times 10^{3}$	$9.513 \times 10^{- 5}$
40	$1.383 \times 10^{3}$	$1.612 \times 10^{- 4}$
50	$8.44 \times 10^{2}$	$1.551 \times 10^{- 4}$
60	$1.022 \times 10^{3}$	$1.344 \times 10^{- 4}$
70	$1.683 \times 10^{3}$	$1.895 \times 10^{- 4}$
80	$1.203 \times 10^{3}$	$1.994 \times 10^{- 4}$
90	$1.083 \times 10^{3}$	$1.794 \times 10^{- 4}$
100	$1.026 \times 10^{3}$	$1.332 \times 10^{- 4}$

Excess Noise	Distance (km)	Improvement
0.01	50	18.2%
0.02	50	30.7%
0.03	25	31.4%
0.04	45	56.3%

Distance	Numerical Method (s)	LSTM (s)
10	$1.743 \times 10^{3}$	$1.753 \times 10^{- 4}$
20	$1.446 \times 10^{3}$	$7.757 \times 10^{- 5}$
30	$1.025 \times 10^{3}$	$9.513 \times 10^{- 5}$
40	$1.383 \times 10^{3}$	$1.612 \times 10^{- 4}$
50	$8.44 \times 10^{2}$	$1.551 \times 10^{- 4}$
60	$1.022 \times 10^{3}$	$1.344 \times 10^{- 4}$
70	$1.683 \times 10^{3}$	$1.895 \times 10^{- 4}$
80	$1.203 \times 10^{3}$	$1.994 \times 10^{- 4}$
90	$1.083 \times 10^{3}$	$1.794 \times 10^{- 4}$
100	$1.026 \times 10^{3}$	$1.332 \times 10^{- 4}$

Abstract

Data availability

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Figures (7)

Tables (2)

Equations (6)

Journal of the Optical Society of America B