"Towards learning quantum states with generative models"
This event is part of the Condensed Matter Theory Seminar Series.
The technological success of machine learning techniques has motivated a research area in the condensed matter physics and quantum information communities, where new tools and conceptual connections between machine learning and many-body physics are rapidly developing. In this talk, I will discuss the use of generative models for learning quantum states. First, I will describe restricted Boltzmann machines (RBM) as a tool to model the wave function of large many-body systems and explore their use in the problem of quantum state tomography of pure states. In the same vein, I will briefly discuss how to learn more general mixed states through a combination of positive-operator valued measures, tensor networks, and generative models. In this setting, generative models enable accurate learning of prototypical quantum states of large size directly from measurements mimicking experimental data.