Automated Machine Learning can Classify Bound Entangled States with Tomograms
arXiv:2001.08118 · doi:10.1007/s11128-021-03037-9
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.
arXiv comment8 pages, 4 figures, 4 tables. Comments are welcome, 1 reference added
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