An intelligent threshold selection method to improve orbital angular momentum-encoded quantum key distribution under turbulence

IF 5.8 2区 物理与天体物理 Q1 OPTICS
Jia-Hao Li, Jie Tang, Xing-Yu Wang, Yang Xue, Hui-Cun Yu, Zhi-Feng Deng, Yue-Xiang Cao, Ying Liu, Dan Wu, Hao-Ran Hu, Ya Wang, Hua-Zhi Lun, Jia-Hua Wei, Bo Zhang, Bo Liu, Lei Shi
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Abstract

High-dimensional quantum key distribution (HD-QKD) encoded by orbital angular momentum (OAM) presents significant advantages in terms of information capacity. However, perturbations caused by free-space atmospheric turbulence decrease the performance of the system by introducing random fluctuations in the transmittance of OAM photons. Currently, the theoretical performance analysis of OAM-encoded QKD systems exists a gap when concerning the statistical distribution under the free-space link. In this article, we analyzed the security of QKD systems by combining probability distribution of transmission coefficient (PDTC) of OAM with decoy-state BB84 method. To address the problem that the invalid key rate is calculated in the part transmittance interval of the post-processing process, an intelligent threshold method based on neural network is proposed to improve OAM-encoded QKD, which aims to conserve computing resources and enhance system efficiency. Our findings reveal that the ratio of root mean square (RMS) OAM-beam radius to Fried constant plays a crucial role in ensuring secure key generation. Meanwhile, the training error of neural network is at the magnitude around 10−3, indicating the ability to predict optimization parameters quickly and accurately. Our work contributes to the advancement of parameter optimization and prediction for free-space OAM-encoded HD-QKD systems. Furthermore, it provides valuable theoretical insights to support the development of free-space experimental setups.

改进湍流条件下轨道角动量编码量子密钥分发的智能阈值选择方法
以轨道角动量(OAM)编码的高维量子密钥分发(HD-QKD)在信息容量方面具有显著优势。然而,自由空间大气湍流造成的扰动会在轨道角动量光子的透射率中引入随机波动,从而降低系统的性能。目前,关于自由空间链路下的统计分布,OAM 编码 QKD 系统的理论性能分析还存在空白。本文结合 OAM 的传输系数概率分布(PDTC)和诱饵状态 BB84 方法,分析了 QKD 系统的安全性。针对后处理过程中在部分传输区间计算无效密钥率的问题,提出了一种基于神经网络的智能阈值方法来改进 OAM 编码 QKD,以达到节约计算资源、提高系统效率的目的。我们的研究结果表明,均方根(RMS)OAM 光束半径与弗里德常数的比值对确保密钥生成的安全性起着至关重要的作用。同时,神经网络的训练误差在 10-3 左右,表明其具有快速准确预测优化参数的能力。我们的工作有助于推动自由空间 OAM 编码 HD-QKD 系统的参数优化和预测。此外,它还为支持自由空间实验装置的开发提供了宝贵的理论见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
EPJ Quantum Technology
EPJ Quantum Technology Physics and Astronomy-Atomic and Molecular Physics, and Optics
CiteScore
7.70
自引率
7.50%
发文量
28
审稿时长
71 days
期刊介绍: Driven by advances in technology and experimental capability, the last decade has seen the emergence of quantum technology: a new praxis for controlling the quantum world. It is now possible to engineer complex, multi-component systems that merge the once distinct fields of quantum optics and condensed matter physics. EPJ Quantum Technology covers theoretical and experimental advances in subjects including but not limited to the following: Quantum measurement, metrology and lithography Quantum complex systems, networks and cellular automata Quantum electromechanical systems Quantum optomechanical systems Quantum machines, engineering and nanorobotics Quantum control theory Quantum information, communication and computation Quantum thermodynamics Quantum metamaterials The effect of Casimir forces on micro- and nano-electromechanical systems Quantum biology Quantum sensing Hybrid quantum systems Quantum simulations.
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