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.

量子机器学习最近引起了量子计算界的广泛关注。在本文中，我们探索了基于量子计算的生成对抗网络（GAN）的能力。更具体地说，我们提出了一种用于生成经典离散分布的量子GAN，它具有经典的量子混合体系结构，由参数化的量子电路（作为生成器）和经典的神经网络（作为鉴别器）组成。参数化量子电路仅由简单的一量子位旋转门和二量子位控制相门组成，这在当前的量子器件中可用。我们的方案具有以下特点和潜在优势：（i）本质上能够生成离散数据（例如文本数据），而传统的GAN由于逐渐消失的梯度问题而显得笨拙。（ii）我们的方案避免了使大多数现有的量子学习算法感到尴尬的输入/输出瓶颈，这些算法要么需要将经典输入数据编码为量子状态，要么输出与解相对应的量子状态，而不是给出解本身，这不可避免损害了量子算法的速度。（iii）可以将数据样本隐式给出的概率分布加载到量子状态，这可能对某些其他应用很有用。或输出对应于解的量子状态而不是给出解本身，这不可避免地会损害量子算法的速度。（iii）可以将数据样本隐式给出的概率分布加载到量子状态，这可能对某些其他应用很有用。或输出对应于解的量子状态而不是给出解本身，这不可避免地会损害量子算法的速度。（iii）可以将数据样本隐式给出的概率分布加载到量子状态，这可能对某些其他应用很有用。