  Quantum machine learning emerges from the symbiosis of quantum mechanics and
machine learning. In particular, the latter gets displayed in quantum sciences
as: (i) the use of classical machine learning as a tool applied to quantum
physics problems, (ii) or the use of quantum resources such as superposition,
entanglement, or quantum optimization protocols to enhance the performance of
classification and regression tasks compare to their classical counterparts.
This paper reviews examples in these two scenarios. On the one hand, a
classical neural network is applied to design a new quantum sensing protocol.
On the other hand, the design of a quantum neural network based on the dynamics
of a quantum perceptron with the application of shortcuts to adiabaticity gives
rise to a short operation time and robust performance. These examples
demonstrate the mutual reinforcement of both neural networks and quantum
physics.
