  In the last few years, quantum computing and machine learning fostered rapid
developments in their respective areas of application, introducing new
perspectives on how information processing systems can be realized and
programmed. The rapidly growing field of Quantum Machine Learning aims at
bringing together these two ongoing revolutions. Here we first review a series
of recent works describing the implementation of artificial neurons and
feed-forward neural networks on quantum processors. We then present an original
realization of efficient individual quantum nodes based on variational
unsampling protocols. We investigate different learning strategies involving
global and local layer-wise cost functions, and we assess their performances
also in the presence of statistical measurement noise. While keeping full
compatibility with the overall memory-efficient feed-forward architecture, our
constructions effectively reduce the quantum circuit depth required to
determine the activation probability of single neurons upon input of the
relevant data-encoding quantum states. This suggests a viable approach towards
the use of quantum neural networks for pattern classification on near-term
quantum hardware.
