LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder

TL;DR

LatentQGAN introduces a hybrid quantum-classical generative adversarial network with an autoencoder, improving scalability and efficiency in quantum data generation tasks, demonstrated on simulators and real quantum hardware.

Contribution

It presents a novel hybrid quantum-classical GAN architecture with an autoencoder, addressing scalability and training issues in quantum generative models.

Findings

Enhanced performance over existing quantum methods

Reduced quantum resource requirements

Effective on both simulators and real quantum hardware

Abstract

Quantum machine learning consists in taking advantage of quantum computations to generate classical data. A potential application of quantum machine learning is to harness the power of quantum computers for generating classical data, a process essential to a multitude of applications such as enriching training datasets, anomaly detection, and risk management in finance. Given the success of Generative Adversarial Networks in classical image generation, the development of its quantum versions has been actively conducted. However, existing implementations on quantum computers often face significant challenges, such as scalability and training convergence issues. To address these issues, we propose LatentQGAN, a novel quantum model that uses a hybrid quantum-classical GAN coupled with an autoencoder. Although it was initially designed for image generation, the LatentQGAN approach holds potential for broader application across various practical data generation tasks. Experimental outcomes on both classical simulators and noisy intermediate scale quantum computers have demonstrated significant performance enhancements over existing quantum methods, alongside a significant reduction in quantum resources overhead.