  The quantum perceptron is a fundamental building block for quantum machine
learning. This is a multidisciplinary field that incorporates abilities of
quantum computing, such as state superposition and entanglement, to classical
machine learning schemes. Motivated by the techniques of shortcuts to
adiabaticity, we propose a speed-up quantum perceptron where a control field on
the perceptron is inversely engineered leading to a rapid nonlinear response
with a sigmoid activation function. This results in faster overall perceptron
performance compared to quasi-adiabatic protocols, as well as in enhanced
robustness against imperfections in the controls.
