Speed-up Quantum Perceptron via Shortcuts to Adiabaticity
arXiv:2003.09938 · doi:10.1038/s41598-021-85208-3
Abstract
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.
arXiv comment7 pages, 5 figures + Supplemental Material
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