| 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 | 530 | 2018 |
| Modelling non-Markovian quantum processes with recurrent neural networks L Banchi, E Grant, A Rocchetto, S Severini New Journal of Physics 20 (12), 123030, 2018 | 96 | 2018 |
| Experimental learning of quantum states A Rocchetto, S Aaronson, S Severini, G Carvacho, D Poderini, I Agresti, ... Science advances 5 (3), eaau1946, 2019 | 82 | 2019 |
| Learning hard quantum distributions with variational autoencoders A Rocchetto, E Grant, S Strelchuk, G Carleo, S Severini npj Quantum Information 4 (1), 28, 2018 | 82 | 2018 |
| Stabilizers as a design tool for new forms of the Lechner-Hauke-Zoller annealer A Rocchetto, SC Benjamin, Y Li Science advances 2 (10), e1601246, 2016 | 53 | 2016 |
| Stabiliser states are efficiently PAC-learnable A Rocchetto Quantum Information and Computation 18 (7&8), 2018 | 44 | 2018 |
| Statistical limits of supervised quantum learning C Ciliberto, A Rocchetto, A Rudi, L Wossnig Physical Review A 102 (4), 042414, 2020 | 16* | 2020 |
| Approximating Hamiltonian dynamics with the Nyström method A Rudi, L Wossnig, C Ciliberto, A Rocchetto, M Pontil, S Severini Quantum 4 (234), 234, 2020 | 15 | 2020 |
| Learning DNFs under product distributions via μ-biased quantum Fourier sampling V Kanade, A Rocchetto, S Severini Quantum Information and Computation Vol. 19, No. 15&16 (2019) 1261–1278 19 …, 2019 | 15 | 2019 |
| Understanding holographic error correction via unique algebras and atomic examples J Pollack, P Rall, A Rocchetto Journal of High Energy Physics, 2022 | 8 | 2022 |
| Decomposition of Pauli groups via weak central products A Rocchetto, FG Russo arXiv preprint arXiv:1911.10158, 2019 | 3 | 2019 |
| Algorithmic models in quantum mechanics A Rocchetto University of Oxford, 2019 | | 2019 |
