基于量子卷积神经网络的ARX分组密码区分器  

ARX Block Cipher Distinguisher Based on Quantum Convolutional Neural Network

作　　者：秦广雪 李丽莎 QIN Guangxue;LI Lisha(School of Cyber Science and Technology,Hubei University,Wuhan 430062,China)

机构地区：[1]湖北大学网络空间安全学院,武汉430062

出　　处：《信息网络安全》2025年第3期467-477,共11页Netinfo Security

基　　金：国家自然科学基金[12101207];湖北省重点研发计划[2023BAB171]。

摘　　要：随着量子计算机的发展,量子神经网络技术不断取得新突破。尽管当前量子计算环境受限,但探索量子神经网络的潜在应用对未来科学技术发展具有重要意义。量子卷积神经网络结合量子计算的优势和神经网络强大的特征提取能力,在二分类任务上表现优异。文章提出一种量子卷积神经区分器,数据特征之间不分块而是作为一个整体编码到量子电路,然后训练参数化量子卷积电路。以SPECK-32为例,使用8个量子比特运行5轮的准确率为76.8%,超越了同等资源条件下的经典区分器,并成功运行到第6轮。文章对比了卷积电路和硬件高效Ansatz作为训练电路的量子神经区分器,结果表明前者具有更高的效率。此外,文章所提区分器成功运行了减轮的Speckey、LAX32、SIMON-32和SIMECK-32算法。最后,分析了影响量子卷积神经区分器性能的因素。With the development of quantum computers,quantum neural network technology continues to make new breakthroughs.Although the current quantum computing environment is still limited,exploring the potential application areas of quantum neural networks is of great significance for the development of future technologies.Quantum convolutional neural networks,which combine the advantages of quantum computing and the powerful feature extraction capabilities of neural networks,have demonstrated excellent performance in binary classification tasks.This paper proposed a quantum convolutional neural distinguisher,in which data features were encoded into the quantum circuit as a whole rather than in multiple partitions,parameterized quantum convolutional circuit was then trained.Taking SPECK-32 as an example,by used 8 qubits,the accuracy of this distinguisher which runned 5 rounds is 76.8%,surpassed the classical distinguisher under the same resource conditions,and successfully runned to 6 rounds.This paper compared quantum neural distinguishers using convolutional circuits and hardware-efficient Ansatz as training circuits,and the results indicate that the former exhibits higher efficiency.In addition,the quantum convolutional distinguisher successfully operated on reduced-round versions of Speckey,LAX32,SIMON-32 and SIMECK-32 algorithms.Finally,factors influencing the performance of the quantum convolutional neural distinguisher were analyzed.

关 键 词：量子卷积神经网络 量子计算 分组密码 区分器 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]