Title: Theory-driven Quantum Machine Learning for HEP
Abstract: Machine Learning is, in most cases, powerful but a black-box application. There have been attempts to create interpretable ansätze by employing graph theory and statistical techniques to understand the learning procedure. In this talk, we will tackle this very same problem from a quantum mechanics point of view, arguing that an optimisation problem, such as classification or anomaly detection, can be studied by "paraphrasing" the problem as a quantum many-body system or a mixed state. Such an approach allows us to employ the entire arsenal of quantum theory for data analysis techniques while enabling exact representation on a quantum device. Hence this talk presents a small step towards fully theory-driven and interpretable quantum machine learning applications.
Zoom Meeting ID: 948 7183 3595