I’m a researcher in quantum machine learning.
I am a Quantum Machine Learning enthusiast and an ELLIS PhD student at Jens Eisert's group in Berlin. I work on 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 ready 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.
I completed a MSc in Physics at the Freie Universität Berlin in 2021, during which I already worked with Jens Eisert and the group. For my Master’s thesis I studied the generalization behavior of re-uploading PQCs from a statistical learning theory perspective. For my Physics Bachelor’s thesis I implemented a quantum simulator and ran supervised learning experiments with PQCs on it, eventually leading to the data re-uploading framework. 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.
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., , Mele A.A., Arzani F., Wilms A., and Eisert J.
PRX Quantum 4, 010328.
Encoding-dependent generalization bounds for parametrized quantum circuits (2021).
Caro M.C., , 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., , and Latorre, J. I.
Quantum 4, 226.
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.
Dahlem Center for Complex Quantum Systems
Freie Universität Berlin