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Stochastic learning control of inhomogeneous quantum ensembles

Turinici, Gabriel (2019), Stochastic learning control of inhomogeneous quantum ensembles, Physical Review. A, Atomic, Molecular and Optical Physics, 100, 5. 10.1103/PhysRevA.100.053403

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Type
Article accepté pour publication ou publié
Date
2019
Nom de la revue
Physical Review. A, Atomic, Molecular and Optical Physics
Volume
100
Numéro
5
Éditeur
American Physical Society
Identifiant publication
Métadonnées
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Auteur(s)
Turinici, Gabriel cc
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Résumé (EN)
In quantum control, the robustness with respect to uncertainties in the system's parameters or driving-field characteristics is of paramount importance and has been studied theoretically, numerically, and experimentally. We test in this paper stochastic search procedures (Stochastic gradient descent and the Adam algorithm) that sample, at each iteration, from the distribution of the parameter uncertainty, as opposed to previous approaches that used a fixed grid. We show that both algorithms behave well with respect to benchmarks and discuss their relative merits. In addition the methodology allows to address high-dimensional parameter uncertainty; we implement numerically, with good results, a three-dimensional and a six-dimensional case.
Mots-clés
Stochastic gradient descent; quantum control