论文标题
通过对绝热性快捷的加速量子感知
Speed-up Quantum Perceptron via Shortcuts to Adiabaticity
论文作者
论文摘要
量子感知器是量子机学习的基本基础。这是一个多学科领域，结合了量子计算的能力，例如状态叠加和纠缠，与经典的机器学习方案。由与绝热性快捷方式的技术激励，我们提出了一个加速量子感知器，其中感知器上的控制场是成反比的，导致具有Sigmoid激活函数的快速非线性响应。与准绝热方案相比，这会导致总体感知性能更快，以及对控件中缺陷的鲁棒性增强。
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.
