DPG Phi
Verhandlungen
Verhandlungen
DPG

Erlangen 2022 – wissenschaftliches Programm

Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

Q: Fachverband Quantenoptik und Photonik

Q 61: Quantum Optics (Miscellaneous)

Q 61.9: Poster

Donnerstag, 17. März 2022, 16:30–18:30, P

Machine learning Lindblad dynamics — •Francesco Carnazza1, Federico Carollo1, Sabine Andergassen1, Dominik Zietlow2, Georg Martius2, and Igor Lesanovsky11Institut für Theoretische Physik and Center for Quantum Science, Universität Tübingen, Auf der Morgenstelle 14, 72076 Tübingen, Germany — 2Max Planck Institute for Intelligent Systems, Max-Planck-Ring 4, 72076 Tübingen, Germany

Even if full knowledge of the wave-function of a quantum system is unattainable, important information can still be retrieved by observing local degrees of freedom. Typically, it is possible to single out and measure just a subsystem, while regarding the rest as a bath. In the simplest case, the evolution of the reduced quantum state obtained by tracing out the environment is governed by a Markovian, i.e. time independent, quantum master equation, also known as Lindblad master equation. Here we investigate if it is possible to train a fully interpretable neural network which learns the parameters of a Lindblad generator [1]. We test this idea in a class of spin models, and investigate in which certain situations the network can indeed provide good predictions.

[1] P. Mazza et    al. Phys. Rev. Research 3, 023084 (2021)

100% | Mobil-Ansicht | English Version | Kontakt/Impressum/Datenschutz
DPG-Physik > DPG-Verhandlungen > 2022 > Erlangen