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arXiv 2504.06384quant-ph

面向受限设备实现的具有可组合安全性和紧密纠错界限的连续变量量子密钥分发

Continuous-Variable Quantum Key Distribution with Composable Security and Tight Error Correction Bound towards Constrained-Device Implementations

University of York(约克大学)

Panagiotis Papanastasiou, Carlo Ottaviani, Stefano Pirandola, Poonam Yadav, Marco Lucamarini

AI总结:

针对受限设备存储和处理能力有限的情况,聚焦非二进制字母表的LDPC码,开发安全框架在最优纠错泄漏极限附近导出有限大小秘密密钥并建模编码内存需求,促进CV-QKD在受限设备中的实际部署。

摘要:

受限设备,如智能传感器、可穿戴设备和物联网节点,在社会中越来越普遍,依赖安全通信正常运行。这些设备常自主运行,在短距离内交换敏感数据或命令。在此背景下,连续变量量子密钥分发(CV-QKD)提供最高安全密钥率及最大通用性以集成到现有基础设施中。在设备存储和处理能力有限的情况下,关键挑战是在可组合安全框架内获得对CV-QKD安全密钥率的现实且紧密估计,其中纠错消耗大部分存储和计算能力。为解决此问题,我们聚焦具有非二进制字母表的低密度奇偶校验(LDPC)码,其优化互信息且特别适用于短距离通信。我们开发一个安全框架以在最优纠错泄漏极限附近导出有限大小的秘密密钥,并对单向纠错中编码过程的相关内存需求进行建模。该分析促进了CV-QKD的实际部署,特别是在存储和计算资源有限的受限设备中。

英文摘要:

Constrained devices, such as smart sensors, wearable devices, and Internet of Things nodes, are increasingly prevalent in society and rely on secure communications to function properly. These devices often operate autonomously, exchanging sensitive data or commands over short distances, such as within a room, house, or warehouse. In this context, continuous-variable quantum key distribution (CV-QKD) offers the highest secure key rate and the greatest versatility for integration into existing infrastructure. A key challenge in this setting, where devices have limited storage and processing capacity, is obtaining a realistic and tight estimate of the CV-QKD secure key rate within a composable security framework, with error correction (EC) consuming most of the storage and computational power. To address this, we focus on low-density parity-check (LDPC) codes with non-binary alphabets, which optimise mutual information and are particularly suited for short-distance communications. We develop a security framework to derive finite-size secret keys near the optimal EC leakage limit and model the related memory requirements for the encoding process in one-way error correction. This analysis facilitates the practical deployment of CV-QKD, particularly in constrained devices with limited storage and computational resources.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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