Ban Yue, Chen Xi, Torrontegui E, Solano E, Casanova J
Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080, Bilbao, Spain.
School of Materials Science and Engineering, Shanghai University, 200444, Shanghai, People's Republic of China.
Sci Rep. 2021 Mar 11;11(1):5783. doi: 10.1038/s41598-021-85208-3.
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.
量子感知器是量子机器学习的基本构建模块。这是一个多学科领域，它将量子计算的能力，如状态叠加和纠缠，融入到经典机器学习方案中。受绝热捷径技术的启发，我们提出了一种加速量子感知器，其中感知器上的控制场是反向设计的，从而导致具有 sigmoid 激活函数的快速非线性响应。与准绝热协议相比，这使得感知器的整体性能更快，同时增强了对控制缺陷的鲁棒性。
