  While many quantum computing techniques for machine learning have been
proposed, their performance on real-world datasets remains to be studied. In
this paper, we explore how a variational quantum circuit could be integrated
into a classical neural network for the problem of detecting pneumonia from
chest radiographs. We substitute one layer of a classical convolutional neural
network with a variational quantum circuit to create a hybrid neural network.
We train both networks on an image dataset containing chest radiographs and
benchmark their performance. To mitigate the influence of different sources of
randomness in network training, we sample the results over multiple rounds. We
show that the hybrid network outperforms the classical network on different
performance measures, and that these improvements are statistically
significant. Our work serves as an experimental demonstration of the potential
of quantum computing to significantly improve neural network performance for
real-world, non-trivial problems relevant to society and industry.
