LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder
This work addresses challenges in quantum machine learning for generating classical data, such as for enriching datasets or finance applications, but it is incremental as it builds on existing QGAN methods.
The paper tackles scalability and training convergence issues in quantum generative adversarial networks (QGANs) by proposing LatentQGAN, a hybrid quantum-classical model with an autoencoder, which demonstrated significant performance enhancements and reduced quantum resource overhead in experiments on simulators and quantum computers.
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.