General Classification of Entanglement Using Machine Learning
Quantum Physics
2022-10-17 v1
Abstract
A classification of multipartite entanglement in qubit systems is introduced for pure and mixed states. The classification is based on the robustness of the said entanglement against partial trace operation. Then we use current machine learning and deep learning techniques to automatically classify a random state of two, three and four qubits without the need to compute the amount of the different types of entanglement in each run; rather this is done only in the learning process. The technique shows high, near perfect, accuracy in the case of pure states. As expected, this accuracy drops, more or less, when dealing with mixed states and when increasing the number of parties involved.
Cite
@article{arxiv.2210.07711,
title = {General Classification of Entanglement Using Machine Learning},
author = {F. El Ayachi and M. El Baz},
journal= {arXiv preprint arXiv:2210.07711},
year = {2022}
}