Machine learning (ML) has demonstrated unprecedented success in predicting complex quantum dynamics and accelerating scientific discovery. Despite these advances, fundamental questions remain: Why do these models succeed? How do they encode the underlying principles of quantum mechanics? Under what conditions can they be interpreted and generalized across diverse quantum systems? Addressing these questions is essential for developing reliable and physically interpretable AI/ML for quantum science.

Our research lies at the interface of quantum science and machine learning. By integrating ML with the fundamental principles of quantum mechanics, we seek to uncover how fundamental laws governing quantum systems are encoded in learned representations, enabling transferable physics-informed AI/ML models. These fundamental insights further guide our efforts in developing quantum-enhanced learning algorithms.