Abstract Quantum generative models provide inherently efficient sampling strategies and thus show promise for achieving an advantage using quantum hardware. In this work. we investigate the barriers to the trainability of quantum generative models posed by barren plateaus and exponential loss concentration. We explore the interplay between explicit and implicit models and losses. https://www.jmannino.com/super-super-University-of-Arizona-Primary-Mark-iPhone-14-Plus-MagSafe-Case-p76978-deal-zone/