TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.15.5.208
TI  - Estimation of the geometric measure of entanglement with Wehrl moments  through artificial neural networks
PY  - 2023/11/27
UR  - https://www.scipost.org/SciPostPhys.15.5.208
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 15
IS  - 5
SP  - 208
A1  - Denis, Jérôme
AU  - Damanet, François
AU  - Martin, John
AB  - 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 accurately predict the geometric measure of entanglement of symmetric multiqubit states using only a limited number of Wehrl moments (moments of the Husimi function of the state) as input, which represents partial information about the state. We consider both pure and mixed quantum states. We compare the results we obtain by training ANNs with the informed use of convergence acceleration methods. We find that even some of the most powerful convergence acceleration algorithms do not compete with ANNs when given the same input data, provided that enough data is available to train these ANNs. We also provide an experimental protocol for measuring Wehrl moments, which is state-independent. More generally, this work opens up perspectives for the estimation of entanglement measures and other SU(2)-invariant quantities, such as the Wehrl entropy, in a way that is more accessible in experiments than by means of full state tomography.
ER  -

@Article{10.21468/SciPostPhys.15.5.208,
	title={{Estimation of the geometric measure of entanglement with Wehrl moments  through artificial neural networks}},
	author={Jérôme Denis and François Damanet and John Martin},
	journal={SciPost Phys.},
	volume={15},
	pages={208},
	year={2023},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.15.5.208},
	url={https://scipost.org/10.21468/SciPostPhys.15.5.208},
}