# Mixed State Entanglement Classification using Artificial Neural Networks

arXiv: https://arxiv.org/abs/2102.06053 · HTML: https://mlsanity.com/paper/2102.06053/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 66 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [Experimental quantum homodyne tomography via machine learning](https://mlsanity.com/paper/1907.06589/) (2019)
- [Quantum Codes from Neural Networks](https://mlsanity.com/paper/1806.08781/) (2018)

## Built on by (most cited first, 3 of 3)

- [Building separable approximations for quantum states via neural networks](https://mlsanity.com/paper/2112.08055/) (2021)
- [Learning coherences from nonequilibrium fluctuations in a quantum heat engine](https://mlsanity.com/paper/2302.13717/) (2023)
- [Seeding neural network quantum states with tensor network states](https://mlsanity.com/paper/2506.23550/) (2025)

## Abstract (arXiv)

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.