Automated Machine Learning can Classify Bound Entangled States with Tomograms
Quantum Physics
2021-09-22 v3
Abstract
For quantum systems with a total dimension greater than six, the positive partial transposition (PPT) criterion is sufficient but not necessary to decide the non-separability of quantum states. Here, we present an Automated Machine Learning approach to classify random states of two qutrits as separable or entangled using enough data to perform a quantum state tomography, without any direct measurement of its entanglement. We could successfully apply our framework even when the Peres-Horodecki criterion fails. In addition, we could also estimate the Generalized Robustness of Entanglement with regression techniques and use it to validate our classifiers.
Cite
@article{arxiv.2001.08118,
title = {Automated Machine Learning can Classify Bound Entangled States with Tomograms},
author = {Caio B. D. Goes and Askery Canabarro and Eduardo I. Duzzioni and Thiago O. Maciel},
journal= {arXiv preprint arXiv:2001.08118},
year = {2021}
}
Comments
8 pages, 4 figures, 4 tables. Comments are welcome, 1 reference added