| Stochastic gradient descent for hybrid quantum-classical optimization R Sweke, F Wilde, J Meyer, M Schuld, PK Fährmann, ... Quantum 4, 314, 2020 | 211 | 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 | 11 | 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 | 11 | 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 | 9 | 2022 |
| Noise can be helpful for variational quantum algorithms (2022) J Liu, F Wilde, AA Mele, L Jiang, J Eisert arXiv preprint arXiv:2210.06723, 0 | 5 | |
| A super-polynomial quantum advantage for combinatorial optimization problems N Pirnay, V Ulitzsch, F Wilde, J Eisert, JP Seifert arXiv preprint arXiv:2212.08678, 2022 | 3 | 2022 |
| Stochastic noise can be helpful for variational quantum algorithms J Liu, F Wilde, AA Mele, L Jiang, J Eisert | | |
