Quantum Computing
Ensuring trustworthiness of quantum AI
As machine learning is deployed in high-stakes , the urgency of increasing its trustworthiness is undeniable. Methods designed to enforce bounded outputs aim to keep model behavior within clearly defined limits, preventing deviations that could lead to severe consequences . A rapidly emerging field of quantum machine learning (QML) inherits these risks and expands both the attack surface and the potential consequences of failure, motivating further research in this area.














