Junyu Liu, Changchun Zhong, Matthew Otten, Anirban Chandra, Cristian L. Cortes...
TL;DR该研究论文探讨了量子机器学习中基于内核的方法,使用神经切向核理论,Kerr 非线性的一阶微扰理论和非微扰数值模拟,以及基于电路 QED 的实验协议来展示在收敛时间和泛化误差方面能够实现一些‘量子增强’。
Abstract
quantum machine learning is a rapidly evolving field of research that could
facilitate important applications for quantum computing and also significantly
impact data-driven sciences. In our work, based on various arguments from
complexity theory and physics, we demonstrate that a sing