NeurIPS 2026 Workshop on SaTQuML: Secure and Trustworthy Quantum Machine Learning

Quantum machine learning (QML) is moving from theoretical promise toward practical experimentation through hybrid quantum-classical platforms, cloud-accessible quantum computers, and early application-driven demonstrations. This transition creates an important need to examine whether QML systems can be secure, trustworthy, and useful in high-impact settings. Our proposed NeurIPS 2026 workshop, SaTQuML: Secure and Trustworthy Quantum Machine Learning, will bring together researchers from QML, trustworthy AI, cybersecurity, and quantum security to address this need. To the best of our knowledge, it is the first workshop at a major machine learning venue centered on the security, trustworthiness, and rigorous evaluation of QML systems. The workshop will focus on two complementary themes--understanding the security and reliability of QML systems themselves, and exploring QML and hybrid quantum-classical methods for cyberdefense and related security applications. By emphasizing realistic benchmarks, strong baselines, clear threat models, reproducibility, and careful assessment of QML's practical value and limitations, SaTQuML aims to build a shared research agenda for trustworthy quantum AI. The broader goal is to help shape a global community around QML systems that can be evaluated responsibly and deployed safely in security-critical environments.