Record link: 
http://hdl.handle.net/20.500.12708/196492
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Title: 
Inferring interpretable dynamical generators of local quantum observables from projective measurements through machine learning
en
Citation: 
Cemin, G., Carnazza, F., Andergassen, S., Martius, G., Carollo, F., & Lesanovsky, I. (2024). Inferring interpretable dynamical generators of local quantum observables from projective measurements through machine learning. Physical Review Applied, 21(4), Article L041001. https://doi.org/10.1103/PhysRevApplied.21.L041001
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Publisher DOI: 
10.1103/PhysRevApplied.21.L041001
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Publication Type: 
Article - Original Research Article
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Language: 
English
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Authors: 
Cemin, Giovanni 
Carnazza, Francesco 
Andergassen, Sabine 
Martius, Georg 
Carollo, Federico 
Lesanovsky, Igor 
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Organisational Unit: 
E194-06 - Forschungsbereich Machine Learning 
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Journal: 
Physical Review Applied 
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ISSN: 
2331-7019
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Date (published): 
3-Apr-2024
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Number of Pages: 
6
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Publisher: 
AMER PHYSICAL SOC
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Peer reviewed: 
Yes
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Keywords: 
Artificial neural networks; Open quantum systems; decoherence; Quantum algorithms; Nonequilibrium lattice models; Quantum many-body systems; Quantum spin models; Lindblad equation; Machine learning
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Research Areas: 
Quantum Many-body Systems Physics: 50%
Modeling and Simulation: 50%
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Science Branch: 
1020 - Informatik: 40%
1030 - Physik, Astronomie: 60%
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