论文标题
神经网络量子性质的实验验证
Experimental verification of the quantum nature of a neural network
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。我建议，由于其基本量子成分，并且由于其功能的规则，因此可以是量子的，因此，由于量子成分的性质和功能性规则，我们都可以获得纠缠，或者是由于类别理论术语，既是由于类别的对象的量子性质和映射的量子。从实际的角度来看，我建议一个可能的实验，可以从原本经典的（从成分的角度）的神经网络的量子功能规则（地图）中提取纠缠。
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. I suggest that a system can be quantum both due to its fundamental quantum constituents and due to the rules of its functioning, therefore, we can obtain entanglement both due to the quantum constituents' nature and due to the functioning rules, or, in category theory terms, both due to the quantum nature of the objects of a category and of the maps. From a practical point of view, I suggest a possible experiment that could extract entanglement from the quantum functioning rules (maps) of an otherwise classical (from the point of view of the constituents) neural network.
