  While the majority of focus in quantum computing has so far been on
monolithic quantum systems, quantum communication networks and the quantum
internet in particular are increasingly receiving attention from researchers
and industry alike. The quantum internet may allow a plethora of applications
such as distributed or blind quantum computing, though research still is at an
early stage, both for its physical implementation as well as algorithms; thus
suitable applications are an open research question. We evaluate a potential
application for the quantum internet, namely quantum federated learning. We run
experiments under different settings in various scenarios (e.g. network
constraints) using several datasets from different domains and show that (1)
quantum federated learning is a valid alternative for regular training and (2)
network topology and nature of training are crucial considerations as they may
drastically influence the models performance. The results indicate that more
comprehensive research is required to optimally deploy quantum federated
learning on a potential quantum internet.
