  Quantum computers are next-generation devices that hold promise to perform
calculations beyond the reach of classical computers. A leading method towards
achieving this goal is through quantum machine learning, especially quantum
generative learning. Due to the intrinsic probabilistic nature of quantum
mechanics, it is reasonable to postulate that quantum generative learning
models (QGLMs) may surpass their classical counterparts. As such, QGLMs are
receiving growing attention from the quantum physics and computer science
communities, where various QGLMs that can be efficiently implemented on
near-term quantum machines with potential computational advantages are
proposed. In this paper, we review the current progress of QGLMs from the
perspective of machine learning. Particularly, we interpret these QGLMs,
covering quantum circuit born machines, quantum generative adversarial
networks, quantum Boltzmann machines, and quantum autoencoders, as the quantum
extension of classical generative learning models. In this context, we explore
their intrinsic relation and their fundamental differences. We further
summarize the potential applications of QGLMs in both conventional machine
learning tasks and quantum physics. Last, we discuss the challenges and further
research directions for QGLMs.
