Deng Wei, Chen Kun, Hua Fei, Cheng Jing, Guo Banghong, Xie Huanwen
Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, School of Optoelectronic Science and Engineering, South China Normal University, Guangzhou 510006, China.
Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, School of Optoelectronic Science and Engineering, South China Normal University, Guangzhou 510006, China.
Entropy (Basel). 2025 Aug 14;27(8):860. doi: 10.3390/e27080860.
As a core component in quantum cryptography, Quantum Random Number Generators (QRNGs) face dual critical challenges: insufficient randomness enhancement and limited compatibility with post-processing algorithms. This study proposes an Adaptive Feedback Compensation Algorithm (AFCA) to address these limitations through dynamic parameter feedback and selective encryption strategies. The AFCA dynamically adjusts nonlinear transformation intensity based on real-time statistical deviations, retaining over 50% of original bits while correcting local imbalances. Experimental results demonstrate significant improvements across QRNG types: the Monobit Test -value for continuous QRNGs increased from 0.1376 to 0.9743, and the 0/1 distribution deviation in discrete QRNGs decreased from 7.9% to 0.5%. Compared to traditional methods like von Neumann correction, AFCA reduces data discard rates by over 55% without compromising processing efficiency. These advancements provide a robust solution for high-security quantum communication systems requiring multi-layered encryption architectures.
作为量子密码学的核心组件，量子随机数发生器（QRNG）面临双重关键挑战：随机性增强不足以及与后处理算法的兼容性有限。本研究提出了一种自适应反馈补偿算法（AFCA），通过动态参数反馈和选择性加密策略来解决这些限制。AFCA基于实时统计偏差动态调整非线性变换强度，在纠正局部不平衡的同时保留超过50%的原始比特。实验结果表明，在各种QRNG类型中都有显著改进：连续QRNG的单比特测试值从0.1376提高到0.9743，离散QRNG中的0/1分布偏差从7.9%降至0.5%。与冯·诺依曼校正等传统方法相比，AFCA在不影响处理效率的情况下，将数据丢弃率降低了超过55%。这些进展为需要多层加密架构的高安全性量子通信系统提供了一个强大的解决方案。
