Quantum Machine Learning Applications in High Energy Physics and Beyond

This talk will review recent applications of quantum machine learning to problems in high energy particle physics motivated by the analysis of data from the Large Hadron Collider at CERN, Geneva. Typical tasks include the classifications of jets as quarks or gluons; the classification of calorimeter clusters as electrons or photons; generative modelling of fragmentation and hadronization in jets; and representation learning. The explored hybrid quantum architectures include: quantum equivariant deep neural networks, quantum equivariant graph neural networks, quantum transformers, quantum diffusion models, quantum GANs, etc.