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arXiv · 2001.08118

Automated Machine Learning can Classify Bound Entangled States with Tomograms

Caio B. D. Goes·Askery Canabarro·Eduardo I. Duzzioni·Thiago O. Maciel
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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.

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Caio B. D. Goes, Askery Canabarro, Eduardo I. Duzzioni, Thiago O. Maciel. 2020-08-25. Automated Machine Learning can Classify Bound Entangled States with Tomograms. https://doi.org/10.1007/s11128-021-03037-9

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