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Article Dans Une Revue Entropy Année : 2023

Characterization of a Driven Two-Level Quantum System by Supervised Learning

Résumé

We investigate the extent to which a two-level quantum systemsubjected to an external time-dependent drive can be characterizedby supervised learning. We apply this approach to the case ofbang-bang control and the estimation of the offset and the finaldistance to a given target state. For any control protocol, thegoal is to find the mapping between the offset and the distance.This mapping is interpolated using a neural network. The estimateis global in the sense that no a priori knowledge is required onthe relation to be determined. Different neural network algorithmsare tested on a series of data sets. We show that the mapping canbe reproduced with very high precision in the direct case when theoffset is known, while obstacles appear in the indirect casestarting from the distance to the target. We point out the limitsof the estimation procedure with respect to the properties of themapping to be interpolated. We discuss the physical relevance ofthe different results.
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Dates et versions

hal-04224779 , version 1 (02-10-2023)

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Citer

Raphael Couturier, Etienne Dionis, Stéphane Guérin, Christophe Guyeux, Dominique Sugny. Characterization of a Driven Two-Level Quantum System by Supervised Learning. Entropy, 2023, 25 (3), pp.446 (15). ⟨10.3390/e25030446⟩. ⟨hal-04224779⟩
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