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Passive Continuous Variable Measurement-Device-Independent Quantum Key Distribution Predictable with Machine Learning in Oceanic Turbulence.

作者信息

Yi Jianmin, Wu Hao, Guo Ying

机构信息

School of Automation, Central South University, Changsha 410083, China.

School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.

出版信息

Entropy (Basel). 2024 Feb 27;26(3):207. doi: 10.3390/e26030207.

DOI:10.3390/e26030207
PMID:38539719
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10969456/
Abstract

Building an underwater quantum network is necessary for various applications such as ocean exploration, environmental monitoring, and national defense. Motivated by characteristics of the oceanic turbulence channel, we suggest a machine learning approach to predicting the channel characteristics of continuous variable (CV) quantum key distribution (QKD) in challenging seawater environments. We consider the passive continuous variable (CV) measurement-device-independent (MDI) QKD in oceanic scenarios, since the passive-state preparation scheme offers simpler linear elements for preparation, resulting in reduced interaction with the practical environment. To provide a practical reference for underwater quantum communications, we suggest a prediction of transmittance for the ocean quantum links with a given neural network as an example of machine learning algorithms. The results have a good consistency with the real data within the allowable error range; this makes the passive CVQKD more promising for commercialization and implementation.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/3cd7e8d0d70d/entropy-26-00207-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/d31f54d90570/entropy-26-00207-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/b6e0b5bfc541/entropy-26-00207-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/36881dc23ebc/entropy-26-00207-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/9e98d8513f20/entropy-26-00207-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/c82e807bc2cf/entropy-26-00207-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/26abc92d2482/entropy-26-00207-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/7fc372fff778/entropy-26-00207-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/3cd7e8d0d70d/entropy-26-00207-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/d31f54d90570/entropy-26-00207-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/b6e0b5bfc541/entropy-26-00207-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/36881dc23ebc/entropy-26-00207-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/9e98d8513f20/entropy-26-00207-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/c82e807bc2cf/entropy-26-00207-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/26abc92d2482/entropy-26-00207-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/7fc372fff778/entropy-26-00207-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139d/10969456/3cd7e8d0d70d/entropy-26-00207-g008.jpg

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本文引用的文献

1
Neural network-based prediction of the secret-key rate of quantum key distribution.基于神经网络的量子密钥分发密钥率预测
Sci Rep. 2022 May 25;12(1):8879. doi: 10.1038/s41598-022-12647-x.
2
Security Analysis of a Passive Continuous-Variable Quantum Key Distribution by Considering Finite-Size Effect.考虑有限尺寸效应的被动连续变量量子密钥分发的安全性分析
Entropy (Basel). 2021 Dec 19;23(12):1698. doi: 10.3390/e23121698.
3
Experimental underwater quantum key distribution.实验性水下量子密钥分发。
Opt Express. 2021 Mar 15;29(6):8725-8736. doi: 10.1364/OE.418323.
4
Practical non-Poissonian light source for passive decoy state quantum key distribution.用于被动诱骗态量子密钥分发的实用非泊松光光源。
Opt Lett. 2010 Oct 15;35(20):3393-5. doi: 10.1364/OL.35.003393.
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Simple and efficient quantum key distribution with parametric down-conversion.基于参量下转换的简单高效量子密钥分发
Phys Rev Lett. 2007 Nov 2;99(18):180503. doi: 10.1103/PhysRevLett.99.180503.
6
Quantum key distribution using gaussian-modulated coherent states.使用高斯调制相干态的量子密钥分发。
Nature. 2003 Jan 16;421(6920):238-41. doi: 10.1038/nature01289.