Unsupervised quantum circuit learning in high energy physics
Andrea Delgado, Kathleen E. Hamilton · Physical review. D/Physical review. D. · 2022
Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. In this work we evaluate the efficacy of using quantum circuit-based generative models to generate synthetic data of high energy physics processes. We use nonadversarial, gradient-based training of quantum circuit Born machines to generate joint distributions over two and three variables.