Sebastian Knebel

Contact

office
Room 1357 at ZIB
e-mail

Curriculum vitae

since 2022
Researcher at ZIB
M.Sc. in Applied Mechanics at TUD
B.Sc. in Mathematics at FUB

Publications and preprints

Preprints and reports

  1. Gelß, P., Klus, S., Knebel, S., Shakibaei, Z., and Pokutta, S. (2022). Low-rank tensor decompositions of quantum circuits. [arXiv]
    [BibTeX]
    @misc{2022_GelssKlusShakibaeiPokutta_Lowranktensordecompositions,
      archiveprefix = {arXiv},
      eprint = {2205.09882},
      arxiv = {arXiv:2205.09882},
      primaryclass = {quant-ph},
      author = {Gelß, Patrick and Klus, Stefan and Knebel, Sebastian and Shakibaei, Zarin and Pokutta, Sebastian},
      title = {Low-rank tensor decompositions of quantum circuits},
      date = {2022-05-19}
    }

Articles, chapters and books

  1. Designolle, S., Iommazzo, G., Besançon, M., Knebel, S., Gelß, P., and Pokutta, S. (2023). Improved local models and new Bell inequalities via Frank-Wolfe algorithms. Physical Review Research, 5(4), 043059. DOI: 10.1103/PhysRevResearch.5.043059 [arXiv] [slides] [code]
    [BibTeX]
    @article{2023_DesignolleEtAl_LocalmodelsBellinequalities,
      year = {2023},
      journal = {Physical Review Research},
      month = oct,
      volume = {5},
      number = {4},
      pages = {043059},
      doi = {10.1103/PhysRevResearch.5.043059},
      archiveprefix = {arXiv},
      eprint = {2302.04721},
      arxiv = {arXiv:2302.04721},
      primaryclass = {quant-ph},
      author = {Designolle, Sébastien and Iommazzo, Gabriele and Besançon, Mathieu and Knebel, Sebastian and Gelß, Patrick and Pokutta, Sebastian},
      title = {Improved local models and new Bell inequalities via Frank-Wolfe algorithms},
      code = {https://github.com/ZIB-IOL/BellPolytopes.jl},
      slides = {https://www.pokutta.com/slides/20230808-tokyo-bell.pdf}
    }

Talks and posters

Research seminar talks

Apr 2025
Koopman von Neumann Mechanics by Sebastian Knebel
IOL Research Seminar (IOL), Berlin

Events

Teaching

winter 2022
Teaching Assistant for Discrete Optimization and Machine Learning at TUB