Speed-up Quantum Perceptron via Shortcuts to Adiabaticity

The quantum perceptron is a fundamental building block in the area of quantum machine learning. This is a multidisciplinary field that incorporates properties 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 the 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 external control.