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Elies Gil-Fuster
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Year
Data re-uploading for a universal quantum classifier
A Pérez-Salinas, A Cervera-Lierta, E Gil-Fuster, JI Latorre
Quantum 4, 226, 2020
6272020
Exploiting symmetry in variational quantum machine learning
JJ Meyer, M Mularski, E Gil-Fuster, AA Mele, F Arzani, A Wilms, J Eisert
PRX Quantum 4 (1), 010328, 2023
1722023
Training quantum embedding kernels on near-term quantum computers
T Hubregtsen, D Wierichs, E Gil-Fuster, PJHS Derks, PK Faehrmann, ...
Physical Review A 106 (4), 042431, 2022
1142022
Encoding-dependent generalization bounds for parametrized quantum circuits
MC Caro, E Gil-Fuster, JJ Meyer, J Eisert, R Sweke
Quantum 5, 582, 2021
1132021
Understanding quantum machine learning also requires rethinking generalization
E Gil-Fuster, J Eisert, C Bravo-Prieto
Nature Communications 15 (1), 2277, 2024
442024
Potential and limitations of random fourier features for dequantizing quantum machine learning
R Sweke, E Recio, S Jerbi, E Gil-Fuster, B Fuller, J Eisert, JJ Meyer
arXiv preprint arXiv:2309.11647, 2023
162023
On the expressivity of embedding quantum kernels
E Gil-Fuster, J Eisert, V Dunjko
Machine Learning: Science and Technology 5 (2), 025003, 2024
132024
On the relation between trainability and dequantization of variational quantum learning models
E Gil-Fuster, C Gyurik, A Pérez-Salinas, V Dunjko
arXiv preprint arXiv:2406.07072, 2024
82024
Concept learning of parameterized quantum models from limited measurements
BY Gan, PW Huang, E Gil-Fuster, P Rebentrost
arXiv preprint arXiv:2408.05116, 2024
12024
Double descent in quantum machine learning
M Kempkes, A Ijaz, E Gil-Fuster, C Bravo-Prieto, J Spiegelberg, ...
arXiv preprint arXiv:2501.10077, 2025
2025
Opportunities and limitations of explaining quantum machine learning
E Gil-Fuster, JR Naujoks, G Montavon, T Wiegand, W Samek, J Eisert
arXiv preprint arXiv:2412.14753, 2024
2024
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