A Quantum Approach to Generative Adversarial Network Data Production

As the landscape of computation increasingly trends toward the development of artificial intelligence and machine learning, Generative Adversarial Networks (GANs) have garnered significant attention for their ability to produce novel high-quality datapoints from a given dataset. However, these networks are limited in scope by the necessity of classically constructed training data. Quantum Generative Adversarial Networks (QGANs), the quantum analog, seem to be a well-suited solution for datasets that are quantum mechanical in nature, and may even offer potential benefits for certain distributions where classical data is better represented in a quantum format.