As the development of quantum computing hardware is on the rise, its potential application to various research areas has been investigated, including to machine learning. Recently, there have been several initiatives to expand the work to quantum federated learning (QFL). However, challenges arise due to the fact that quantum computation poses different characteristics from classical computation, giving an even more challenge for a federated setting. In this paper, we present a high-level overview of the current state of research in QFL. Furthermore, we also describe in brief about quantum computation and discuss its present limitations in relation to QFL development. Additionally, possible approaches to deploy QFL are explored. Lastly, remarks and challenges of QFL are also presented.