Quantum Machine Learning: Perspectives in Cybersecurity

被引：0

作者：

Pastorello, Davide ^{[1
,2
]}

机构：

[1] Univ Bologna, Dept Math, Piazza Porta San Donato 5, I-40126 Bologna, Italy

[2] TIFPA INFN, Via Sommarive 14, I-38123 Povo, TN, Italy

来源：

COMPUTER SAFETY, RELIABILITY, AND SECURITY. SAFECOMP 2024 WORKSHOPS | 2024年 / 14989卷

关键词：

Quantum machine learning; cybersecurity; data corruption; gradient masking;

D O I：

10.1007/978-3-031-68738-9_20

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this work, we give an overview on some recent results related to quantum machine learning (QML) regarding the training of quantum generative adversarial neural networks by means of classical shadows, and a parametric model implemented on a quantum annealer. Then, we argue that QML models can be robust against targeted data corruption and gradient-based attacks, motivating the exploration of the relationship between QML and cybersecurity.

引用

页码：266 / 274

页数：9

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