  Quantum machine learning has recently attracted much attention from the
community of quantum computing. In this paper, we explore the ability of
generative adversarial networks (GANs) based on quantum computing. More
specifically, we propose a quantum GAN for generating classical discrete
distribution, which has a classical-quantum hybrid architecture and is composed
of a parameterized quantum circuit as the generator and a classical neural
network as the discriminator. The parameterized quantum circuit only consists
of simple one-qubit rotation gates and two-qubit controlled-phase gates that
are available in current quantum devices. Our scheme has the following
characteristics and potential advantages: (i) It is intrinsically capable of
generating discrete data (e.g., text data), while classical GANs are clumsy for
this task due to the vanishing gradient problem. (ii) Our scheme avoids the
input/output bottlenecks embarrassing most of the existing quantum learning
algorithms that either require to encode the classical input data into quantum
states, or output a quantum state corresponding to the solution instead of
giving the solution itself, which inevitably compromises the speedup of the
quantum algorithm. (iii) The probability distribution implicitly given by data
samples can be loaded into a quantum state, which may be useful for some
further applications.
