改进量子随机数生成器的实时后处理

IF 4.4 Q1 OPTICS Advanced quantum technologies Pub Date : 2024-04-29 DOI:10.1002/qute.202400025
Qian Li, Xiaoming Sun, Xingjian Zhang, Hongyi Zhou
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引用次数: 0

摘要

随机性提取是密码学和理论计算机科学中的一个关键问题。随着近年来量子密码学的快速发展,针对量子对手存在时的安全问题,量子防随机性提取也得到了广泛研究。与将输入原始数据表征为最小熵源的传统防量子随机性提取器相比,我们发现由一大类可信设备量子随机数发生器生成的输入原始数据可以表征为所谓的反向块源。这一事实使我们能够设计出改进的提取器。针对反向块源,我们特别提出了两种新型防量子随机性提取器,可实现实时分块提取。与一般的最小熵随机性提取器相比,在种子长度相同的情况下,它们的提取速度明显更快,输出数据长度也更长。此外,它们还具有在线算法的特性,即无需等待获得全部输入原始数据,即可对原始数据进行快速处理。这些特点使该设计成为对实用量子随机数发生器进行实时后处理的适当选择。将提取器应用于由广泛使用的量子随机数发生器生成的原始数据,模拟提取速度高达 300 Gbps。
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Improved Real-Time Post-Processing for Quantum Random Number Generators

Randomness extraction is a key problem in cryptography and theoretical computer science. With the recent rapid development of quantum cryptography, quantum-proof randomness extraction has also been widely studied, addressing the security issues in the presence of a quantum adversary. In contrast with conventional quantum-proof randomness extractors characterizing the input raw data as min-entropy sources, it is found that the input raw data generated by a large class of trusted-device quantum random number generators can be characterized as the so-called reverse block source. This fact enables us to design improved extractors. Two novel quantum-proof randomness extractors for reverse block sources that realize real-time block-wise extraction are proposed specifically. In comparison with the general min-entropy randomness extractors, the designs achieve a significantly higher extraction speed and a longer output data length with the same seed length. In addition, they enjoy the property of online algorithms, which process the raw data on the fly without waiting for the entire input raw data to be available. These features make the design an adequate choice for the real-time post-processing of practical quantum random number generators. Applying the extractors to the raw data generated by a widely used quantum random number generator, a simulated extraction speed as high as 300 Gbps is achieved.

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来源期刊
CiteScore
7.90
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0.00%
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期刊最新文献
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