Quantum Adversarial Machine Learning

Understand

Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of machine learning approaches in adversarial settings and developing techniques accordingly to make learning robust to adversarial manipulations.

• It plays a vital role in various machine learning applications and has attracted tremendous attention across different communities recently.
• In this paper, we explore different adversarial scenarios in the context of quantum machine learning.
• We find that, similar to traditional classifiers based on classical neural networks, quantum learning systems are likewise vulnerable to crafted adversarial examples, independent of whether the input data is classical or quantum.