Statistical Limits of Supervised Quantum Learning, Physical Review A 102, 042414 (2020). With C. Ciliberto, A. Rudi, L. Wossnig [arXiv link]
Decomposition of Pauli groups via weak central products, arXiv preprint arXiv:1911.10158 (2020). With F. Russo [arXiv link]
Learning DNFs under product distributions via μ-biased quantum Fourier sampling, Quantum Information and Computation, Vol. 19, No. 15&16 (2019). With V. Kanade and S. Severini [arXiv link]
Experimental learning of quantum states, Science Advances 5, No. 3, eaau1946 (2019). With S. Aaronson, I. Agresti, M. Bentivegna, G. Carvacho, D. Poderini, and S. Severini [arXiv link]
Stabiliser states are efficiently PAC learnable, Quantum Information and Computation, Vol. 18, No. 7&8 (2018) [arXiv link]
Quantum machine learning: a classical perspective, Proceedings of the Royal Society A 474, No. 2209 (2018). With C. Ciliberto, M. Herbster, A. D. Ialongo, M. Pontil, S. Severini, and L. Wossnig [arXiv link]
Learning hard quantum distributions with variational autoencoders, npj Quantum Information, 4 (2018). With G. Carleo, E. Grant, S. Severini, and S. Strelchuk [arXiv link]
Modelling non-Markovian quantum processes with recurrent neural networks, New Journal of Physics, Vol. 20, No. 12 (2018). With L. Banchi, E. Grant, and S. Severini [arXiv link]
Approximating Hamiltonian dynamics with the Nyström method, arXiv preprint arXiv: 1804.02484 (2018). With C. Ciliberto, M. Pontil, A. Rudi, S. Severini, and L. Wossnig [arXiv link]
Stabilizers as a design tool for new forms of the Lechner-Hauke-Zoller annealer, Science Advances 2, No. 10, e1601246 (2016). With S. Benjamin and Y. Li [arXiv link]