Quantum Machine Learning and the Journey to Its Scalability

Quantum systems are well known to create non-classical patterns. The thought that they could also be used to recognize highly complex patterns hidden in data is beyond excitement, leading to the young interdisciplinary field of quantum machine learning (QML). Nevertheless, while a quantum advantage in data analysis can be in principle achieved thanks to the exponentially large Hilbert space, the scalability of QML models has been in heated debates. The very same exponential large space can at the same time hinder the scalability if it is poorly handled. In this talk, I will give an overview of QML and discuss its scalability challenges including barren plateaus, exponential concentration, classical simulability.

Supanut did his PhD at Centre for Quantum Technologies (CQT), NUS, Singapore. After that, he did a post-doc with Zoë Holmes at EPFL in quantum computation and quantum machine learning. He is now a faculty member at Chulalongkorn University, Thailand.