Variational Quantum Circuits Enhanced Generative Adversarial Network
This work addresses the problem of high computational costs in GAN training for researchers and practitioners in quantum machine learning, though it is incremental as it builds on existing quantum-classical hybrid methods.
The authors tackled the computational expense of training GANs by proposing a hybrid quantum-classical architecture (QC-GAN) for hand-written image generation, achieving better performance (Frechet Inception Distance) with fewer parameters and iterations compared to classical GANs and outperforming an alternative quantum GAN.
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.