BriefGPT.xyz
Jan, 2024
对抗性量子机器学习:一种信息论的普遍化分析
Adversarial Quantum Machine Learning: An Information-Theoretic Generalization Analysis
HTML
PDF
Petros Georgiou, Sharu Theresa Jose, Osvaldo Simeone
TL;DR
这篇论文研究了对抗训练的量子分类器在受限制的白盒攻击下的泛化特性,通过使用攻击感知的或对抗的损失函数进行训练,对量子对手最大化分类器的损失,得到了对抗训练的量子分类器的集成误差的新的信息理论上限,并验证了理论结果在合成环境中的数值实验。
Abstract
In a manner analogous to their classical counterparts,
quantum classifiers
are vulnerable to
adversarial attacks
that perturb their inputs. A promising countermeasure is to train the quantum classifier by adoptin
→