Sofiene Jerbi, MSc

PhD Student

Room: 4S13 ITP
Phone: +43 512 507 52245
Email: Sofiene.Jerbi[at]uibk.ac.at

Research group: Quantum Information and Computation

More Information

  • FLD Research Documentation

    Publications 2022

    Contributions to Books / Journals

    Journal Article (Original Paper)
    • Poulsen Nautrup, H.; Metger, T.; Iten, R.; Jerbi, S.; Trenkwalder, L.; Wilming, H.; Briegel, Hans J.; Renner, R. (2022): Operationally meaningful representations of physical systems in neural networks.
      In: Machine Learning: Science and Technology 3/4, Nr. 045025. (DOI) (Web link)

    • Skolik, A.; Jerbi, S.; Dunjko, V. (2022): Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning.
      In: Quantum. The Open Journal for Quantum Science 6, No. 720. (DOI) (Web link)

    Proceedings Article (Full Paper)
    • Cornelissen, A.; Hamoudi, Y.; Jerbi, S. (2022): Near-optimal quantum algorithms for multivariate mean estimation.
      In: Leonardi, S.; Gupta, A.: STOC 2022: Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing. New York: Association for Computing Machinery., ISBN 978-1-4503-9264-8, pp. 33 - 43. (DOI) (Web link)

    Other Publications

    Electronic Publication (Preprint)
    • Jerbi, S.; Cornelissen, A.; Ozols, M.; Djunko, V (2022): Quantum policy gradient algorithms. (Web link)

    • Patel, Y. J.; Jerbi, S.; Bäck, T.; Dunjko, V. (2022): Reinforcement learning assisted recursive QAOA. (Web link)



    Lectures 2022

    Presentations at Conferences, Symposia, etc.

    Conference Lecture (Upon Registration)
    • Lecturer(s): Jerbi, S.: Unifying quantum machine learning models: theory and practical implications.
      Quantum Techniques in Machine Learning - QTML 2022, Neapel, 2022-11-11. (Web link)

    • Lecturer(s): Jerbi, S.: Quantum machine learning beyond kernel methods.
      International Conference on Quantum Optics, Obergurgl, 2022-02-25. (Web link)

    Poster Presentation
    • Lecturer(s): Jerbi, S.: Unifying quantum machine learning models: theory and practical implications.
      DK-ALM Summer School 2022, Lech, 2022-09-14. (Web link)

    • Lecturer(s): Jerbi, S.: Unifying quantum machine learning models: theory and practical implications.
      SFB BeyondC Conference 2022 - Frontiers of Quantum Information Science, Wien, 2022-09-05. (Web link)

    • Lecturer(s): Jerbi, S.: Unifying quantum machine learning models: theory and practical implications.
      Measurement-Based Quantum Computation, Learning and Agency (MBQC 2022), Obergurgl, 2022-09-01. (Web link)

    • Lecturer(s): Jerbi, S.: Variational quantum circuits for reinforcement learning.
      International Conference on Quantum Optics, Obergurgl, 2022-02-22. (Web link)



    Publications 2021

    Contributions to Books / Journals

    Journal Article (Original Paper)
    • Jerbi, S.; Trenkwalder, Lea; Poulsen Nautrup, H.; Briegel, Hans J.; Dunjko, Vedran (2021): Quantum Enhancements for Deep Reinforcement Learning in Large Spaces.
      In: PRX Quantum 2/1, No. 010328. (Full-text) (DOI) (Web link)

    Proceedings Article (Full Paper)
    • Jerbi, S.; Gyurik, C.; Marshall, S. C.; Briegel, Hans J.; Dunjko, V. (2021): Parametrized quantum policies for reinforcement learning.
      In: Ranzato, M.; Beygelzimer, A.; LIang, P.S.; Vaughan, J.W.; Dauphin, Y.: 35th Conference on Neural Information Processing Systems. Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021). Monday December 6 through Tuesday December 14, 2021. Virtual-only Conference. San Diego: Neural Information Processing Systems Foundation., online. (Web link)

    Other Publications

    Electronic Publication (Preprint)
    • Cornelissen, A.; Jerbi, S. (2021): Quantum algorithms for multivariate Monte Carlo estimation. (DOI) (Web link)

    • Jerbi, S.; Fiderer, L.; Poulsen Nautrup, H.; Kübler, J.; Briegel, Hans J.; Dunjko, V. (2021): Quantum machine learning beyond kernel methods. (DOI) (Web link)



    Lectures 2021

    Presentations at Conferences, Symposia, etc.

    Conference Lecture (Upon Registration)
    • Lecturer(s): Jerbi, S.: Parametrized quantum circuits for reinforcement learning.
      Quantum Techniques for Machine Learning (QTML) 2021, Tokyo, online, 2021-11-10. (Web link)

    • Lecturer(s): Jerbi, S.: Variational quantum policies for reinforcement learning.
      Gemeinsame Jahrestagung der SPS und ÖPG 2021 (Swiss and Austrian Physical Societies), Innsbruck, 2021-09-02. (Web link)

    Lecture at Summer-/Winterschool
    • Lecturer(s): Jerbi, S.: Quantum-enhanced reinforcement learning.
      Summer School: Machine Learning in Quantum Physics and Chemistry 2021, Warschau, 2021-08-31. (Web link)

    • Lecturer(s): Jerbi, S.: Variational quantum policies for reinforcement learning.
      IMPRS-MPHQ-BeyondC online Summer School 2021, Wien, online, 2021-07-15. (Web link)

    Poster Presentation
    • Lecturer(s): Jerbi, S.: Parametrized quantum policies for reinforcement learning.
      Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), San Diego, 2021-12-08. (Web link)

    • Lecturer(s): Jerbi, S.: Variational quantum algorithms for reinforcement learning.
      DK-Alm Summer School, Pertisau, 2021-07-14. (Web link)



    Publications 2020

    Contributions to Books / Journals

    Journal Article (Original Paper)
    • Flamini, Fulvio; Hamann, Arne; Jerbi, S.; Trenkwalder, Lea; Poulsen Nautrup, H.; Briegel, Hans J. (2020): Photonic architecture for reinforcement learning.
      In: New Journal of Physics 22, No. 045002. (Full-text) (DOI) (Web link)



    Lectures 2020

    Presentations at Conferences, Symposia, etc.

    Conference Lecture (Invited Lecture)
    • Lecturer(s): Flamini, Fulvio Co-author(s): Hamann, A.; Jerbi, S.; Trenkwalder, L.; Poulsen Nautrup, H.; Briegel, Hans J.: Photonic architecture for reinforcement learning.
      22nd Photonics North Conference, Québec, 2020-05-26. (Web link)

    Conference Lecture (Upon Registration)
    • Lecturer(s): Jerbi, S: Quantum enhancements for deep reinforcement learning in large spaces.
      Quantum Techniques in Machine Learning / QTML 2020, Cambridge, Massachusetts, 2020-11-12. (Web link)



    Lectures 2019

    Presentations at Conferences, Symposia, etc.

    Conference Lecture (Invited Lecture)
    • Lecturer(s): Flamini, Fulvio Co-author(s): Hamann, A.; Jerbi, S.; Trenkwalder, L.; Poulsen Nautrup, H.; Briegel, Hans J.: Photonic architecture for reinforcement learning.
      1. DPG Herbsttagung 2019 on Quantum Science and Information Technologies, Freiburg, 2019-09-23. (Web link)

    • Lecturer(s): Jerbi, S. Co-author(s): Poulsen Nautrup, Hendrik; Trenkwalder, Lea; Briegel, Hans J.; Vedran, Dunjko: A framework for deep energy-based reinforcement learning with quantum speed-up.
      1. DPG Herbsttagung 2019 on Quantum Science and Information Technologies, Freiburg, 2019-09-23. (Web link)

    Poster Presentation
    • Lecturer(s): Flamini, Fulvio Co-author(s): Hamann, A.; Jerbi, S.; Trenkwalder, L. M.; Poulsen Nautrup, H; Briegel, Hans J.: Photonic architecture for reinforcement learning.
      Austrian Quantum Information Conference 2019, Wien, 2019-10-31. (Web link)

    • Lecturer(s): Flamini, Fulvio Co-author(s): Hamann, A.; Jerbi, S.; Trenkwalder, L.; Poulsen Nautrup, H.; Briegel, Hans J.: Photonic architecture for reinforcement learning.
      Vienna Graduate Conference on Complex Quantum Systems, Wien, 2019-10-28. (Web link)

    • Lecturer(s): Jerbi, S. Co-author(s): Poulsen Nautrup, H.; Trenkwalder, L.; Briegel, Hans J.; Dunjko, V.: A framework for deep energy-based reinforcement learning with quantum speed-up.
      Quantum Techniques in Machine Learning (QTML2019), Daejeon, 2019-10-22. (Web link)

    • Lecturer(s): Jerbi, S. Co-author(s): Poulsen Nautrup, H.; Trenkwalder, L.M.; Briegel, Hans J.; Dunjko, V.: A framework for deep energy-based reinforcement learning with quantum speed-up.
      Austrian Quantum Information Conference 2019, Wien, 2019-10-31. (Web link)

Nach oben scrollen