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态空间的探测，作为波函数的近似，并避免量子蒙特卡洛方法中反复出现的符号问题。人们可能会问，通常的经典神经网络是否具有某些实际的隐藏量子特性，使其成为高度耦合量子问题的合适工具。本文探讨了使系统具有量子性的原因，以及我们在何种程度上可以将神经网络解释为具有量子残余。我认为，一个系统之所以是量子的，既可以源于其基本量子组分，也可以源于其运行规则；因此，我们既可以从量子组分的本质中获得纠缠，也可以从运行规则中获得纠缠，或者用范畴论的术语来说，既可以源于范畴中对象的量子性质，也可以源于映射的量子性质。从实践的角度出发，我提出了一种可能的实验方案，该实验可以从一个在组分层面为经典、但运行规则（映射）具有量子特性的神经网络中提取纠缠。