Variations of Architectures and Applications of Quantum Generative Adversarial Networks

Abstract

Generative Adversarial Networks (GANs) are generative models that function as a minimax game, in which a generative network and a discriminative network compete against each other with the goal of creating data that convincingly resembles the real sample. With the advent of Quantum Computing and the development of Quantum Machine Learning (QML) models, Quantum Generative Adversarial Networks (QuGANs) have been increasingly studied due to the possible advantages this new type of architecture can offer, especially regarding performance improvements, scalability, and the exploration of new applications. In this context, this study's guiding question is: how were QuGANs developed between the years 2018 and 2025? To answer this question, the general objective of this work is to conduct a systematic literature review of QuGANs during the proposed period. Using a systematic literature review as the methodological basis, articles published and available on the online platforms Lens, Scopus, and Web of Science were selected, and the Rayyan tool was employed to identify duplicate works and those that did not specifically address QuGANs. As a result, a prevalence of hybrid models was observed, in which the developed architecture integrates quantum and classical characteristics in a complementary manner. Regarding the type of application, approaches involving theoretical foundations and image generation stand out as the most common. Other application areas are also explored (chemistry and pharmaceuticals, quantum error correction, high-energy physics, experimental implementation, medical applications, cloud computing, anomaly detection, telecommunications, biometrics, finance, physics and simulations, security and cryptography, software engineering, noise in QuGANs, and survey), demonstrating the broad potential of QuGANs across various research fields and industrial sectors.