| Stochastic gradient descent for hybrid quantum-classical optimization R Sweke, F Wilde, J Meyer, M Schuld, PK Fährmann, ... Quantum 4, 314, 2020 | 229 | 2020 |
| Noise can be helpful for variational quantum algorithms J Liu, F Wilde, AA Mele, L Jiang, J Eisert arXiv preprint arXiv:2210.06723, 2022 | 23 | 2022 |
| Scalably learning quantum many-body Hamiltonians from dynamical data F Wilde, A Kshetrimayum, I Roth, D Hangleiter, R Sweke, J Eisert arXiv preprint arXiv:2209.14328, 2022 | 18 | 2022 |
| Single-component gradient rules for variational quantum algorithms T Hubregtsen, F Wilde, S Qasim, J Eisert Quantum Science and Technology 7 (3), 035008, 2022 | 10 | 2022 |
| An in-principle super-polynomial quantum advantage for approximating combinatorial optimization problems via computational learning theory N Pirnay, V Ulitzsch, F Wilde, J Eisert, JP Seifert Science Advances 10 (11), eadj5170, 2024 | 7* | 2024 |
