Generative adversarial network (GAN) is one of the widely-adopted
machine-learning frameworks for a wide range of applications such as generating
high-quality images, video, and audio contents. However, training a GAN could
become computationally expensive for large neural networks. In this work, we
propose a hybrid quantum-classical architecture for improving GAN (denoted as
QC-GAN). The performance was examed numerically by benchmarking with a
classical GAN using MindSpore Quantum on the task of hand-written image
generation. The generator of the QC-GAN consists of a quantum variational
circuit together with a one-layer neural network, and the discriminator
consists of a traditional neural network. Leveraging the entangling and
expressive power of quantum circuits, our hybrid architecture achieved better
performance (Frechet Inception Distance) than the classical GAN, with much
fewer training parameters and number of iterations for convergence. We have
also demonstrated the superiority of QC-GAN over an alternative quantum GAN,
namely pathGAN, which could hardly generate 16$\times$16 or larger images. This
work demonstrates the value of combining ideas from quantum computing with
machine learning for both areas of Quantum-for-AI and AI-for-Quantum.

提出了一种用于改进生成对抗网络（GAN）的混合量子 - 经典架构（QC-GAN），通过与传统神经网络和量子变分电路的结合，实现了比经典 GAN 更好的性能，在迭代次数和训练参数方面也更加高效。该研究同时展示了将量子计算与机器学习相结合在量子 - 人工智能领域和人工智能 - 量子领域的价值。

变分量子电路增强生成对抗网络

Variational Quantum Circuits Enhanced Generative Adversarial Network

Variational quantum circuits are used in quantum machine learning and
variational quantum simulation tasks. Designing good variational circuits or
predicting how well they perform for given learning or optimization tasks is
still unclear. Here we discuss these problems, analyzing variational quantum
circuits using the theory of neural tangent kernels. We define quantum neural
tangent kernels, and derive dynamical equations for their associated loss
function in optimization and learning tasks. We analytically solve the dynamics
in the frozen limit, or lazy training regime, where variational angles change
slowly and a linear perturbation is good enough. We extend the analysis to a
dynamical setting, including quadratic corrections in the variational angles.
We then consider hybrid quantum-classical architecture and define a large-width
limit for hybrid kernels, showing that a hybrid quantum-classical neural
network can be approximately Gaussian. The results presented here show limits
for which analytical understandings of the training dynamics for variational
quantum circuits, used for quantum machine learning and optimization problems,
are possible. These analytical results are supported by numerical simulations
of quantum machine learning experiments.

本研究通过分析神经切向核理论，定义了量子神经切向核，推导出其与损失函数之间的动力学方程，探讨了基于变分量子电路的量子机器学习和优化问题的培训动态的分析结果和数值模拟实验的支持结果。