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Stochastic learning control of inhomogeneous quantum ensembles

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stoch_quantum_ensemble_control_Turinici_2019.pdf (685.3Kb)
Date
2019
Dewey
Analyse
Sujet
Stochastic gradient descent; quantum control
Journal issue
Physical Review. A, Atomic, Molecular and Optical Physics
Volume
100
Number
5
Publication date
11-2019
Publisher
American Physical Society
DOI
http://dx.doi.org/10.1103/PhysRevA.100.053403
URI
https://basepub.dauphine.fr/handle/123456789/20358
Collections
  • CEREMADE : Publications
Metadata
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Author
Turinici, Gabriel
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Type
Article accepté pour publication ou publié
Abstract (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.

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