This chapter aims to explore the convergence of two emerging fields of computer science: quantum computing and Generative Adversarial Networks. Though capable of individually performing heavy tasks themselves, we would try to understand how their interlinking can prove to be a beneficial path that may lead to the birth of different breakthroughs in coming times. Quantum computing, which can process large amounts of information simultaneously in high-dimensional data spaces, offers a promising solution to some of the limitations faced by classical learning models. We begin by reviewing the fundamentals of quantum computing, followed by an overview of GANs and their applications in generative tasks. Then, we dive into the field of QuantumGANs, examining key algorithms and architectures. Current challenges are also discussed, along with future developments.