SAMOP 2023 – scientific programme

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QI: Fachverband Quanteninformation

QI 25: Quantum Entanglement II

QI 25.3: Talk

Thursday, March 9, 2023, 11:30–11:45, B302

Entanglement from Wehrl Moments using Deep Learning — •Jérôme Denis, François Damanet, and John Martin — University of Liège

In recent years, artificial neural networks (ANNs) have become an increasingly popular tool for studying problems in quantum theory, and in particular entanglement theory. In this work, we analyse to what extent ANNs can provide us with an accurate estimate of the geometric measure of entanglement of pure and mixed symmetric multiqubit states on the basis of a few moments of the Husimi function (Wehrl moments) of the state. We compare the results we obtain by training ANNs with the use of convergence acceleration methods and find that these algorithms do not compete with ANNs when given the same input data. This opens up perspectives for the estimation of SU(2) invariant quantities that should be more easily accessible in experiments than full state tomography.