Hi, I’m Elies Gil-Fuster

I’m a researcher in quantum machine learning.

About

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I’m a PhD student working on Quantum Machine Learning (QML) in Jens Eisert's group in Berlin, I also have half-a-leg in Vedran Dunjko's group in Leiden. During 2025, I spent time in Zürich, where I worked on Quantum Optimization in Stefan Woerner's group at IBM Research. My main goal is to contribute to the mathematical foundations of Quantum Machine Learning theory. I aim to combine tools from modern ML theory, quatum complexity theory and quantum algorithms. Long term, I’d like to combine these with insights from complexity science.

If you’d like to get in touch, please drop me an email or find me on Bluesky, as @eliesgf.

You can find more information in my CV. A complete list of publications can be found in my Google Scholar profile.

Publications

Peer-reviewed

  • Potential and limitations of random Fourier features for dequantizing quantum machine learning (2025).
    Sweke R., Recio E., Jerbi S., Gil-Fuster E., Fuller B., Eisert J., and Meyer J. J.
    Quantum 9, 1640.

  • On the relation between trainability and dequantization of variational quantum learning models (2025).
    Gil-Fuster E., Gyurik C., Pérez-Salinas A., and Dunjko V.
    ICLR 2025, arXiv:2406.07072.

  • On the expressivity of embedding quantum kernels (2024).
    Gil-Fuster E., Eisert J., and Dunjko V.
    Mach. Learn.: Sci. Technol. 5 025003.

  • Understanding quantum machine learning also requires rethinking generalization (2024).
    Gil-Fuster E., Eisert J., and Bravo-Prieto C.
    Nat. Comms. 15, 2277.

  • Exploiting symmetry in variational quantum machine learning (2023)
    Meyer J.J., Mularski M., Gil-Fuster E., Mele A.A., Arzani F., Wilms A., and Eisert J.
    PRX Quantum 4, 010328.

  • Training Quantum Embedding Kernels on Near-Term Quantum Computers (2022)
    Hubregtsen T., Wierichs D., Gil-Fuster E., Derks P.J.H.S., Faehrmann P.K., and Meyer J. J.
    PRA 106, 042431, arXiv:2105.02276.

  • Encoding-dependent generalization bounds for parametrized quantum circuits (2021).
    Caro M.C., Gil-Fuster E., Meyer J.J., Eisert J., and Sweke R.
    Quantum 5, 582.

  • Data re-uploading for a universal quantum classifier (2020).
    Pérez-Salinas A., Cervera-Lierta A., Gil-Fuster E., and Latorre J. I.
    Quantum 4, 226.

Pre-prints

  • Quantum computing and artificial intelligence: status and perspectives (2025).
    Acampora G., …, Gil-Fuster E., …,
    arXiv:2505.23860.

  • Kernel-based dequantization of variational QML without Random Fourier Features (2025).
    Sweke R., Shin S., Gil-Fuster E.
    arXiv:2503.23931.

  • Double descent in quantum machine learning (2025).
    Kempkes M., Ijaz A., Gil-Fuster E., Bravo-Prieto C., Spiegelberg J. van Nieuwenburg E., Dunjko V.
    arXiv:2501.10077.

  • Opportunities and limitations of explaining quantum machine learning (2024).
    Gil-Fuster E., Naujoks J.R., Montavon G., Wiegand T., Samek W., Eisert J.
    arXiv:2412.14753.

  • Concept learning of parameterized quantum models from limited measurements (2024).
    Gan B.Y., Huang P.-W., Gil-Fuster E., Rebentrost P.
    arXiv:2408.05116.

Conferences & talks

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Conferences

  • 2024.XI | Talk: On the expressivity of embedding quantum kernels | QTML2024.

  • 2024.IX | Poster: On the relation between trainability and dequantization of variational quantum learning models | SeeQA 2024.

  • 2024.IV | Talk: Understanding quantum machine learning also requires rethinking generalization | QCTiP2024.

  • 2024.IV | Poster: On the expressivity of embedding quantum kernels | QCTiP2024.

  • 2024.III | Talk: On the expressivity of embedding quantum kernels | DPG March meeting 2024.

  • 2024.III | Talk: Understanding quantum machine learning also requires rethinking generalization | DPG March meeting 2024.

  • 2024.I | Poster: Understanding quantum machine learning also requires rethinking generalization | QIP2024.

  • 2023.XI | Talk: Understanding quantum machine learning also requires rethinking generalization | QTML2023.

  • 2022.XI | Poster: Non-Embedding Quantum Kernels, do they exist? | QTML2022.

  • 2021.XI | Talk: Encoding-dependent generalization bounds for parametrized quantum circuits | QTML2021.

  • 2020.III | Poster: Data re-uploading for a universal quantum classifier | QCTIP2020.

Seminars and workshops

Contact

Elies Gil-Fuster
Dahlem Center for Complex Quantum Systems
Freie Universität Berlin
Arnimallee 14
14195 Berlin
Germany