I’m a researcher in quantum machine learning.
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. My main goal is to derive meaningful statements for quantum circuits seen as machine learning models. I aim to combine the mathematical theory of machine learning together with insights from quantum algorithms to push the field of QML beyond near-term trends. My everyday research is concerned with parametrized quantum circuits and quantum kernels, as well as their expressivity, trainability, and generalization capacity.
If you’d like to get in touch, please drop me an email or find me on Twitter, as @EliesMiquel.
You can find more information in my CV. A complete list of publications can be found in my Google Scholar profile.
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.
On the relation between trainability and dequantization of variational quantum learning models (2024).
Gil-Fuster E., Gyurik C., Pérez-Salinas A., and Dunjko V.
arXiv:2406.07072.
Potential and limitations of random Fourier features for dequantizing quantum machine learning (2023).
Sweke R., Recio E., Jerbi S., Gil-Fuster E., Fuller B., Eisert J., and Meyer J. J.
arXiv:2309.11647.
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.
2024.VII | Seminar: On the relation between trainability and dequantization of variational quantum learning models | QTI Journal Club at CERN.
2024.IV | Seminar: On the expressivity of embedding quantum kernels | GIQ Seminar at UAB.
2024.II | Seminar: On the expressivity of embedding quantum kernels | IBM Q UK seminar.
2024.I | Seminar: On the expressivity of embedding quantum kernels | QAISG QML seminar.
2023.XII | Seminar: On the expressivity of embedding quantum kernels | aQa seminar at Leiden University.
2023.XI | Seminar: On the expressivity of embedding quantum kernels | QIC seminar at EPFL.
2023.XI | Seminar: Understanding quantum machine learning also requires rethinking generalization | Einstein Research Unit update meeting.
2023.X | Seminar: Understanding quantum machine learning also requires rethinking generalization | Google Quantum AI QML meeting.
2023.X | Seminar: Understanding quantum machine learning also requires rethinking generalization | QAISG QML seminar.
2023.IX | Seminar: Understanding quantum machine learning also requires rethinking generalization | QUANTIC group seminar.
2023.VI | Participated: Quantum Information workshop | Centro de ciencias de Benasque Pedro Pascual.
2023.IV | Workshop talk: Exploiting symmetry in variational quantum machine learning | Machine Learning and (Quantum) Physics workshop.
2023.I | Seminar: Exploiting symmetry in variational quantum machine learning | QUANTIC group seminar.
2021.XII | Workshop talk: Training quantum embedding kernels on near-term quantum computers | “Theory of Quantum Machine Learning”.
2021.XI | Seminar: Encoding-dependent generalization bounds for parametrized quantum circuits | Tomamichel Group Seminar.
2021.IX | Workshop poster: Generalization bounds in the quantum realm | EDS2021.
2020.V | Participated: Hands-on Quantum Computing Summer School | Centro de ciencias de Benasque Pedro Pascual.
Elies Gil-Fuster
Dahlem Center for Complex Quantum Systems
Freie Universität Berlin
Arnimallee 14
14195 Berlin
Germany