  Reliable methods for the classification and quantification of quantum
entanglement are fundamental to understanding its exploitation in quantum
technologies. One such method, known as Separable Neural Network Quantum States
(SNNS), employs a neural network inspired parameterisation of quantum states
whose entanglement properties are explicitly programmable. Combined with
generative machine learning methods, this ansatz allows for the study of very
specific forms of entanglement which can be used to infer/measure entanglement
properties of target quantum states. In this work, we extend the use of SNNS to
mixed, multipartite states, providing a versatile and efficient tool for the
investigation of intricately entangled quantum systems. We illustrate the
effectiveness of our method through a number of examples, such as the
computation of novel tripartite entanglement measures, and the approximation of
ultimate upper bounds for qudit channel capacities.
