Comprehensive Analysis on Image Generation using Quantum Generative Adversarial Network

Images are a very useful form of information from medical diagnosis to scientific discovery. There are some areas where images are not enough but with the help of Generative Adversarial Networks (GANs) this problem is solved at some limit. Due to the success of classical generative adversarial networks, the quantum researcher created quantum version of GAN and also called as quantum generative adversarial network (QGANs) which works on quantum computers which use quantum mechanics techniques such as quantum entanglement, superposition, teleportation and many more. The successful implementation in real life of quantum computers are not easy because of limited quantum resources and access to quantum processors but instead of that there are different QGAN models for different purpose, such as drug discovery, finance, image generation and many more which shown good result as compare to their classical counterparts. This study is reviewed based on authors that proposed different QGAN models along with the techniques proposed on those papers for generating images, which datasets was considered, which parameters are considered, what their limitations and how to future research directions to overcome those problems.