all AI news
A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models
April 26, 2024, 4:41 a.m. | Maximilian Wendlinger, Kilian Tscharke, Pascal Debus
cs.LG updates on arXiv.org arxiv.org
Abstract: Quantum machine learning (QML) continues to be an area of tremendous interest from research and industry. While QML models have been shown to be vulnerable to adversarial attacks much in the same manner as classical machine learning models, it is still largely unknown how to compare adversarial attacks on quantum versus classical models. In this paper, we show how to systematically investigate the similarities and differences in adversarial robustness of classical and quantum models using …
abstract adversarial adversarial attacks analysis arxiv attacks comparative analysis cs.cr cs.lg industry machine machine learning machine learning models qml quant-ph quantum research robustness type vulnerable while
More from arxiv.org / cs.LG updates on arXiv.org
Sliced Wasserstein with Random-Path Projecting Directions
1 day, 6 hours ago |
arxiv.org
The Un-Kidnappable Robot: Acoustic Localization of Sneaking People
1 day, 6 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US
Research Engineer
@ Allora Labs | Remote
Ecosystem Manager
@ Allora Labs | Remote
Founding AI Engineer, Agents
@ Occam AI | New York