Ban, YueChen, XiTorrontegui Muñoz, ErikSolano, EnriqueCasanova, Jorge2021-04-052021-04-052020-03-11Scientific reports, 11, article number 5783, March 2021, 8 pp.2045-2322https://hdl.handle.net/10016/32264The 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.8eng© The Author(s) 2021Tis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material.Atribución 3.0 EspañaSpeed limitQubitsQuantum controlAdiabaticityPerceptronresearch articleFísicahttps://doi.org/10.1038/s41598-021-85208-3open access157838Scientific Reports11AR/0000026546