Dr. Lukas Fiderer

More Information

• FLD Research Documentation

  Publications 2024

  Contributions to Books / Journals

  Journal Article (Original Paper)

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    Caraglio, M.; Kaur, H.; Fiderer, L.; Lopez-Incera, A.; Briegel, H.J.; Franosch, T.; Munoz-Gil, G. (2024): Learning how to find targets in the micro-world: the case of intermittent active Brownian particles.
    In: Soft Matter 20/9, pp. 2008 - 2016. (DOI) (Web link)
    
    
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    Flamini, F.; Krumm, M.; Fiderer, L.; Müller, T.; Briegel, Hans J. (2024): Reinforcement learning and decision making via single-photon quantum walks.
    In: Quantum Science and Technology 9/4, No. 045011. (DOI) (Web link)
    
    
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    Flamini, F.; Krumm, M.; Fiderer, L.; Müller, T.; Briegel, Hans J. (2024): Towards interpretable quantum machine learning via single-photon quantum walks.
    In: Quantum Science and Technology 9/4, No. 045011. (DOI) (Web link)
    
    

  
  
  

  Lectures 2024

  Presentations at Conferences, Symposia, etc.

  Conference Lecture (Upon Registration)

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    Lecturer(s): Smith, I. Co-author(s): Krumm, M.; Fiderer, L.J.; Poulsen Nautrup, H; Briegel, H.J.: The Min-Entropy of Classical-Quantum Combs for Measurement-Based Applications.
    87. Jahrestagung der DPG und DPG-Frühjahrstagung, Berlin, 2024-03-21. (Web link)
    
    

  
  
  

  Publications 2023

  Contributions to Books / Journals

  Journal Article (Original Paper)

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    Jerbi, S.; Fiderer, L.; Poulsen Nautrup, H.; Kübler, J.; Briegel, Hans J.; Dunjko, V. (2023): Quantum machine learning beyond kernel methods.
    In: Nature Communications 14, No. 517. (DOI) (Web link)
    
    
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    Smith, I. D.; Krumm, M.; Fiderer, L. J.; Poulsen Nautrup, H.; Briegel, H. J. (2023): The Min-Entropy of Classical-Quantum Combs for Measurement-Based Applications.
    In: Quantum. The Open Journal for Quantum Science 7, Nr. 1206. (DOI) (Web link)
    
    

  
  
  

  Lectures 2023

  Presentations at Conferences, Symposia, etc.

  Poster Presentation

  • 
    Lecturer(s): Flamini, F. Co-author(s): Krumm, M.; Fiderer, L.; Müller, T.; Briegel, Hans J.: Towards interpretable quantum machine learning via single-photon quantum walks.
    Workshop "Innsbruck-Konstanz meeting on physics and philosophy", Bregenz, 2023-09-18.
    
    

  
  
  

  Publications 2022

  Contributions to Books / Journals

  Journal Article (Original Paper)

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    Morris, B.; Fiderer, L.; Lang, B.; Goldwater, D. (2022): Witnessing Bell violations through probabilistic negativity.
    In: Physical Review A (Atomic, Molecular and Optical Physics) 105/3, Nr. 032202. (DOI) (Web link)
    
    

  
  
  

  Lectures 2022

  Presentations at Conferences, Symposia, etc.

  Conference Lecture (Upon Registration)

  • 
    Lecturer(s): Smith, I. Co-author(s): Krumm, Marius; Fiderer, Lukas; Nautrup, Hendrik Poulsen; Briegel, Hans: A Quantum Causal Perspective of Measurement-Based Quantum Computation.
    Quantum Physics and Logic 2022, Oxford, 2022-06-30. (Web link)
    
    

  Poster Presentation

  • 
    Lecturer(s): Fiderer, L.: Quantum Machine Learning Beyond Kernel Methods: A unified picture for quantum models.
    5th Seefeld Workshop on Quantum Information, Seefeld, 2022-06-28. (Web link)
    
    
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    Lecturer(s): Fiderer, L.: Quantum Machine Learning Beyond Kernel Methods: A unified picture for quantum models.
    International Conference on Quantum Optics, Obergurgl, 2022-02-22. (Web link)
    
    

  
  
  

  Lectures 2021

  Presentations at Conferences, Symposia, etc.

  Conference Lecture (Upon Registration)

  • 
    Lecturer(s): Fiderer, L.: Smart Quantum Sensors: Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation.
    Gemeinsame Jahrestagung der SPS und ÖPG 2021 (Swiss and Austrian Physical Societies), Innsbruck, 2021-09-02. (Web link)
    
    
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    Lecturer(s): Fiderer, L.: Witnessing Bell violations through probabilistic negativity.
    Gemeinsame Jahrestagung der SPS und ÖPG 2021 (Swiss and Austrian Physical Societies), Innsbruck, 2021-09-02. (Web link)
    
    
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    Lecturer(s): Fiderer, L.: Smart Quantum Sensors: Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation.
    Machine Learning for Quantum 2021 (MLQ), Ulm (online), 2021-03-03. (Web link)