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Quantum federated learning (QFL) is a novel framework that integrates theadvantages of classical federated learning (FL) with the computational power ofquantum technologies. This includes quantum computing and quantum machinelearning (QML), enabling QFL to handle high-dimensional complex data. QFL canbe deployed over both classical and quantum communication networks in order tobenefit from information-theoretic security levels surpassing traditional FLframeworks. In this paper, we provide the first comprehensive investigation ofthe challenges and opportunities of QFL. We particularly examine the keycomponents of QFL and identify the unique challenges that arise when deployingit over both classical and quantum networks. We then develop novel solutionsand articulate promising research directions that can help address theidentified challenges. We also provide actionable recommendations to advancethe practical realization of QFL.

Foundations of Quantum Federated Learning Over Classical and Quantum Networks