Quantum Federated Learning (QFL) is an intriguing intersection of quantum computing (QC) and federated learning (FL), two cutting-edge fields in technology. FL involves training machine learning (ML) models across decentralized devices that hold local datasets without exchanging them centrally. However, only model updates (gradients) are shared with the central server. In QFL, QC principles are applied to enhance the efficiency and security of FL protocols. This article presents a comprehensive study on QFL. Our goal is to provide a full understanding of the fundamentals, such as the basic terminologies of QC, the primary framework of QFL, various research challenges associated with QFL, privacy-preservation techniques in QFL, and applications of QFL. We provide an overview of the current state of research in this rapidly developing area, describe the problems and opportunities connected with the integration of these technologies, and outline the future directions and open research issues that are now being investigated.