  Deep learning is one of the most successful and far-reaching strategies used
in machine learning today. However, the scale and utility of neural networks is
still greatly limited by the current hardware used to train them. These
concerns have become increasingly pressing as conventional computers quickly
approach physical limitations that will slow performance improvements in years
to come. For these reasons, scientists have begun to explore alternative
computing platforms, like quantum computers, for training neural networks. In
recent years, variational quantum circuits have emerged as one of the most
successful approaches to quantum deep learning on noisy intermediate scale
quantum devices. We propose a hybrid quantum-classical neural network
architecture where each neuron is a variational quantum circuit. We empirically
analyze the performance of this hybrid neural network on a series of binary
classification data sets using a simulated universal quantum computer and a
state of the art universal quantum computer. On simulated hardware, we observe
that the hybrid neural network achieves roughly 10% higher classification
accuracy and 20% better minimization of cost than an individual variational
quantum circuit. On quantum hardware, we observe that each model only performs
well when the qubit and gate count is sufficiently small.
