  Quantum mechanics is inherently probabilistic in light of Born's rule. Using
quantum circuits as probabilistic generative models for classical data exploits
their superior expressibility and efficient direct sampling ability. However,
training of quantum circuits can be more challenging compared to classical
neural networks due to lack of efficient differentiable learning algorithm. We
devise an adversarial quantum-classical hybrid training scheme via coupling a
quantum circuit generator and a classical neural network discriminator
together. After training, the quantum circuit generative model can infer
missing data with quadratic speed up via amplitude amplification. We
numerically simulate the learning and inference of generative adversarial
quantum circuit using the prototypical Bars-and-Stripes dataset. Generative
adversarial quantum circuits is a fresh approach to machine learning which may
enjoy the practically useful quantum advantage on near-term quantum devices.
