I’m a researcher in quantum machine learning.
I am a Quantum Machine Learning (QML) enthusiast and an ELLIS PhD student at Jens Eisert's group in Berlin. I obtained a Google PhD Fellowship for Quantum Computing on October 2023. I completed a MSc in Physics at the Freie Universität Berlin in 2021, during which I already worked with Jens Eisert and the group. I work to develop the theory of Parametrized Quantum Circuits (PQCs) as quantum learning models, mostly for supervised learning with classical data. If you want to hear about them, I’m always up for a chat! I have half-a-leg at the Fraunhofer Heinrich-Hertz Institut in Berlin, where I wonder about eXplainable Artificial Intelligence (XAI), and the other half at Vedran Dunjko's group in Leiden, where I’ll be spending some months in the coming years.
Before starting the PhD, I gathered some experience in research with a Master’s thesis on generalization bounds for PQCs, and a Bachelor’s thesis introducing the framework of data re-uploading for QML. I also spent a summer in 2021 working with Maria Schuld as a research resident in Xanadu Inc., we did some exploration in exploiting the Fourier picture of re-uploading for quantum kernels.
Apart from my research I contribute to science communication as a volunteer assistant for the insideQuantum podcast, managing communications and scheduling the intervew guests. I have contributed to open-source software by writing documentation and fixing bugs on the PennyLane webpage. I’m an avid city-walker and reader (mostly English and American fiction from the last 200 years), a big boardgame fan, and an easy target for nerd-snipes and mathy riddles.
For more information, here’s my CV, my Google Scholar, and my email :)
Ah! And you can of course also find me on Twitter, as @EliesMiquel.
Elies is currently an ELLIS PhD student at Jens Eisert's group in Berlin, he is also co-supervised by Vedran Dunjko in Leiden. Before, he obtained a MSc in Physics at Freie Universität Berlin, and a BSc in Physics and a BSc in Mathematics at Universitat de Barcelona. His research interests focus on building a theory for quantum machine learning with parametrized quantum circuits, with emphasis on the data re-uploading framework and quantum kernel functions.
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 expressivity of embedding quantum kernels (2023).
Gil-Fuster E., Eisert J., and Dunjko V.
arXiv:2309.14419.
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.
Understanding quantum machine learning also requires rethinking generalization (2023).
Gil-Fuster E., Eisert J., and Bravo-Prieto C.
arXiv:2306.13461.
2024.I | Invited: On the expressivity of embedding quantum kernels | QAISG QML seminar.
2024.I | Poster: Understanding quantum machine learning also requires rethinking generalization | QIP2024.
2023.XII | Invited: On the expressivity of embedding quantum kernels | aQa seminar at Leiden University.
2023.XI | Invited: On the expressivity of embedding quantum kernels | QIC seminar at EPFL.
2023.XI | Contributed: Understanding quantum machine learning also requires rethinking generalization | QTML2023.
2023.XI | Invited: Understanding quantum machine learning also requires rethinking generalization | Einstein Research Unit update meeting.
2023.X | Invited: Understanding quantum machine learning also requires rethinking generalization | Google Quantum AI QML meeting.
2023.X | Invited: Understanding quantum machine learning also requires rethinking generalization | QAISG QML seminar.
2023.IX | Invited: 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 | Contributed: Exploiting symmetry in variational quantum machine learning | Machine Learning and (Quantum) Physics workshop.
2023.I | Invited: Exploiting symmetry in variational quantum machine learning | QUANTIC group seminar.
2022.XI | Poster: Non-Embedding Quantum Kernels, do they exist? | QTML2022.
2021.XII | Invited: Training quantum embedding kernels on near-term quantum computers | One-day workshop: “Theory of Quantum Machine Learning”.
2021.XI | Contributed: Encoding-dependent generalization bounds for parametrized quantum circuits | QTML2021.
2021.XI | Invited: Encoding-dependent generalization bounds for parametrized quantum circuits | Tomamichel Group Seminar.
2021.IX | Poster: Generalization bounds in the quantum realm | EDS2021.
2020.V | Participated: Hands-on Quantum Computing Summer School | Centro de ciencias de Benasque Pedro Pascual.
2020.III | Poster: Data re-uploading for a universal quantum classifier | QCTIP2020.
Elies Gil-Fuster
Dahlem Center for Complex Quantum Systems
Freie Universität Berlin
Arnimallee 14
14195 Berlin
Germany