My current research focuses on understanding the origin of discrepancies between the theoretical capacity of artificial neural networks to represent quantum states (Neural Quantum States) in Quantum Many-Body Physics and their effective, practical performance.

More information about Sergio García Heredia

I am strongly convinced that the relationship between Physics and Machine Learning forms a virtuous cycle: learning algorithms can expand our ability to study complex phenomena, while physical principles can guide the development of better learning methods. For this reason, I maintain a broad interest in areas such as Physics-Informed Machine Learning, Geometric Deep Learning, and Scientific Machine Learning more generally. From time to time, I publish posts on these topics on the following personal blog: https://www.scibits.blog/