I’m a researcher in quantum machine learning.
I’m a PhD student in Jens Eisert's group in Berlin, I also work with Vedran Dunjko’s group in Leiden. I work on Quantum Machine Learning (QML), at the intersection between machine learning theory and quantum computing. My research interests are asking what the theory of supervised learning can tell us about learning models based on quantum circuits. My everyday research is concerned with parametrized quantum circuits, quantum kernels, and their expressivity, trainability, and generalization capacity.
You can find more information in my CV. A complete list of publications can be found in my Google Scholar profile.
If you’d like to get in touch, you can drop me an email or find me on Twitter, as @EliesMiquel.
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