Andrea Rocchetto
TitleCited byYear
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
532018
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
222016
Learning hard quantum distributions with variational autoencoders
A Rocchetto, E Grant, S Strelchuk, G Carleo, S Severini
npj Quantum Information 4 (1), 28, 2018
152018
Experimental learning of quantum states
A Rocchetto, S Aaronson, S Severini, G Carvacho, D Poderini, I Agresti, ...
Science advances 5 (3), eaau1946, 2019
92019
Stabiliser states are efficiently PAC-learnable
A Rocchetto
Quantum Information and Computation 18 (7&8), 2018
82018
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
42018
Learning DNFs under product distributions via {\mu}-biased quantum Fourier sampling
V Kanade, A Rocchetto, S Severini
arXiv preprint arXiv:1802.05690, 2018
12018
Approximating Hamiltonian dynamics with the Nystr\" om method
A Rudi, L Wossnig, C Ciliberto, A Rocchetto, M Pontil, S Severini
arXiv preprint arXiv:1804.02484, 2018
2018
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Articles 1–8