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.

翻译：神经网络正被用于改进多粒子系统状态空间的探测，作为波函数的近似形式，并规避量子蒙特卡洛中反复出现的符号问题。人们或许会追问：传统经典神经网络是否实际存在某些隐藏的量子特性，使其成为处理强耦合量子问题的适宜工具？本文探讨了系统具备量子性的判定标准，以及我们能在何种程度上将神经网络解释为具有量子残留特征。我认为系统既可能因其基本量子组分的性质而呈现量子性，也可能因其运行规则而具有量子性；相应地，量子纠缠既可能源于量子组分的本质属性，也可能来自运行规则的作用——用范畴论语言表述，这既可能是范畴中对象的量子性，也可能是映射的量子性。从实践角度，我提出了一项可能的实验方案，该方案能够从经典神经网络（就组分而言）的量子运行规则（映射）中提取量子纠缠。