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基于SPECK-32/64的量子神经网络区分器

Hyunji Kim¹, Kyungbae Jang¹, Sejin Lim¹

¹Division of IT Convergence Engineering, Hansung University, Seoul 02876, Republic of Korea.

Sensors (Basel, Switzerland)

|July 8, 2023

概括

本研究介绍了SPECK-32区块密码的第一个量子神经网络区分器,证明了它的潜力,尽管目前量子计算的局限性. 虽然它没有超过经典方法,但它显示了量子密码学未来进步的希望.

科学领域:

量子计算和密码学量子计算和密码学
机器学习在网络安全中的应用

背景情况:

像SPECK-32这样的轻量级块密码对于保护物联网 (IoT) 传感器数据至关重要.
深度学习已经成为分析区块密码差异特征的强大工具.
量子计算的进步需要探索用于加密分析的量子机器学习.

研究的目的:

提出和评估第一个基于量子神经网络 (QNN) 的区分器,用于噪音中介尺度量子 (NISQ) 时代内的SPECK-32块密码.
在受约束的量子计算环境下分析QNN区分器的性能.
调查各种QNN参数对区分器性能的影响.

主要方法:

为SPECK-32块密码量身定制的量子神经区分器的开发.
在NISQ设备上对QNN区分器进行实验评估,评估其准确性和操作范围 (最多5轮).
与经典深度学习区分器进行比较分析.
深入分析影响QNN性能的因素,包括嵌入方法,量子比特数和量子层.

主要成果:

量子神经区分器成功运行了多达5轮的SPECK-32.
达到0.53的精度,证明了其作为区分器的能力 (精度>0.51),尽管有局限性.
由于当前的量子硬件限制,经典的神经区分器实现了更高的精度 (0.93).
确定了影响性能的关键QNN参数 (嵌入,量子位数,层),突出了超出资源扩展范围的仔细调整的需要.

结论:

拟议的量子神经区分器是NISQ时代QNN应用于轻量级区块密码加密分析的开创性一步.
与经典方法相比,当前的量子硬件限制限制了性能,但区分器证明是功能性的.
未来对量子资源和参数优化的改进对于增强基于QNN的加密区分器至关重要.

关键词:

在SPECK-32/64中,使用的是SPECK-32/64 这是一个差异差异的差异.区分器区分器量子神经网络是一个量子神经网络.

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