This talk will overview recent developments in combining symbolic reasoning algorithms with deep generative models. We will use probabilistic circuits as the architecture that bridges learning and reasoning. These circuits represent joint distributions as deep computation graphs. They move beyond other deep generative models and probabilistic graphical models by guaranteeing tractable exact probabilistic and logical inference for certain classes of queries: marginal probabilities, symbolic conditioning, expectations, entropies, causal effects, etc. Probabilistic circuit models are now also effectively learned from data at scale, and achieve state-of-the-art results in constrained sampling from both language models and natural image distributions, as well as other neuro-symbolic tasks.