论文标题
经典的神经网络是量子吗？
Are classical neural networks quantum?
论文作者
论文摘要
神经网络被用来改善许多粒子系统的状态空间的探测，以避免量子蒙特卡洛的重复符号问题。人们可能会问通常的经典神经网络是否具有一些实际的隐藏量子特性，使它们成为适合高度耦合量子问题的合适工具。我在这里讨论是什么使系统量子变成量子，以及我们可以在多大程度上将神经网络解释为具有量子残留物。
Neural networks are being used to improve the probing of the state spaces of many particle systems as approximations to wavefunctions and in order to avoid the recurring sign problem of quantum monte-carlo. One may ask whether the usual classical neural networks have some actual hidden quantum properties that make them such suitable tools for a highly coupled quantum problem. I discuss here what makes a system quantum and to what extent we can interpret a neural network as having quantum remnants.
