  Quantum federated learning (QFL) is a novel framework that integrates the
advantages of classical federated learning (FL) with the computational power of
quantum technologies. This includes quantum computing and quantum machine
learning (QML), enabling QFL to handle high-dimensional complex data. QFL can
be deployed over both classical and quantum communication networks in order to
benefit from information-theoretic security levels surpassing traditional FL
frameworks. In this paper, we provide the first comprehensive investigation of
the challenges and opportunities of QFL. We particularly examine the key
components of QFL and identify the unique challenges that arise when deploying
it over both classical and quantum networks. We then develop novel solutions
and articulate promising research directions that can help address the
identified challenges. We also provide actionable recommendations to advance
the practical realization of QFL.
