| The variational quantum eigensolver: a review of methods and best practices J Tilly, H Chen, S Cao, D Picozzi, K Setia, Y Li, E Grant, L Wossnig, ... Physics Reports 986, 1-128, 2022 | 1493 | 2022 |
| Quantum machine learning: a classical perspective C Ciliberto, M Herbster, AD Ialongo, M Pontil, A Rocchetto, S Severini, ... Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2018 | 679 | 2018 |
| An initialization strategy for addressing barren plateaus in parametrized quantum circuits E Grant, L Wossnig, M Ostaszewski, M Benedetti Quantum 3, 214, 2019 | 618 | 2019 |
| Quantum linear system algorithm for dense matrices L Wossnig, Z Zhao, A Prakash Physical review letters 120 (5), 050502, 2018 | 319 | 2018 |
| Quantum gradient descent and Newton's method for constrained polynomial optimization P Rebentrost, M Schuld, L Wossnig, F Petruccione, S Lloyd https://arxiv.org/abs/1612.01789, 2017 | 223 | 2017 |
| Quantum linear systems algorithms: a primer D Dervovic, M Herbster, P Mountney, S Severini, N Usher, L Wossnig arXiv preprint arXiv:1802.08227, 2018 | 155 | 2018 |
| Adversarial quantum circuit learning for pure state approximation M Benedetti, E Grant, L Wossnig, S Severini New Journal of Physics 21 (4), 043023, 2019 | 111 | 2019 |
| Universal discriminative quantum neural networks H Chen, L Wossnig, S Severini, H Neven, M Mohseni Quantum Machine Intelligence 3 (1), 1, 2021 | 102 | 2021 |
| Dynamical mean field theory algorithm and experiment on quantum computers I Rungger, N Fitzpatrick, H Chen, CH Alderete, H Apel, A Cowtan, ... arXiv preprint arXiv:1910.04735, 2019 | 83 | 2019 |
| Computation of molecular excited states on IBM quantum computers using a discriminative variational quantum eigensolver J Tilly, G Jones, H Chen, L Wossnig, E Grant Physical Review A 102 (6), 062425, 2020 | 48 | 2020 |
| Quantum state discrimination using noisy quantum neural networks A Patterson, H Chen, L Wossnig, S Severini, D Browne, I Rungger Physical Review Research 3 (1), 013063, 2021 | 40 | 2021 |
| Cost-function embedding and dataset encoding for machine learning with parametrized quantum circuits S Cao, L Wossnig, B Vlastakis, P Leek, E Grant Physical Review A 101 (5), 052309, 2020 | 38 | 2020 |
| Generative training of quantum Boltzmann machines with hidden units N Wiebe, L Wossnig arXiv preprint arXiv:1905.09902, 2019 | 36 | 2019 |
| A quantum algorithm for simulating non-sparse Hamiltonians C Wang, L Wossnig arXiv preprint arXiv:1803.08273, 2018 | 36 | 2018 |
| Best practices for machine learning in antibody discovery and development L Wossnig, N Furtmann, A Buchanan, S Kumar, V Greiff Drug Discovery Today 29 (7), 104025, 2024 | 24 | 2024 |
| Quantum machine learning for classical data L Wossnig arXiv preprint arXiv:2105.03684, 2021 | 18 | 2021 |
| Statistical limits of supervised quantum learning C Ciliberto, A Rocchetto, A Rudi, L Wossnig arXiv preprint arXiv:2001.10477, 2020 | 17 | 2020 |
| Approximating Hamiltonian dynamics with the Nyström method A Rudi, L Wossnig, C Ciliberto, A Rocchetto, M Pontil, S Severini Quantum 4, 234, 2020 | 15 | 2020 |
| Quantum relative entropy training of boltzmann machines NO Wiebe, LP WOSSNIG US Patent App. 16/289,417, 2020 | 6 | 2020 |
| Fast quantum learning with statistical guarantees C Ciliberto, A Rocchetto, A Rudi, L Wossnig arXiv preprint arXiv:2001.10477, 2020 | 5 | 2020 |
Simone SeveriniVerified email at ucl.ac.uk
