  Quantum Federated Learning (QFL) is an emerging interdisciplinary field that
merges the principles of Quantum Computing (QC) and Federated Learning (FL),
with the goal of leveraging quantum technologies to enhance privacy, security,
and efficiency in the learning process. Currently, there is no comprehensive
survey for this interdisciplinary field. This review offers a thorough,
holistic examination of QFL. We aim to provide a comprehensive understanding of
the principles, techniques, and emerging applications of QFL. We discuss the
current state of research in this rapidly evolving field, identify challenges
and opportunities associated with integrating these technologies, and outline
future directions and open research questions. We propose a unique taxonomy of
QFL techniques, categorized according to their characteristics and the quantum
techniques employed. As the field of QFL continues to progress, we can
anticipate further breakthroughs and applications across various industries,
driving innovation and addressing challenges related to data privacy, security,
and resource optimization. This review serves as a first-of-its-kind
comprehensive guide for researchers and practitioners interested in
understanding and advancing the field of QFL.
