Giacomo Torlai, Ph.D.

Flatiron Research Fellow, CCQ, Flatiron Institute

Giacomo Torlai joined the Center for Computational Quantum Physics at the Flatiron Institute in October 2018. His research at CCQ focuses on the development of novel computational methods based on machine learning algorithms to investigate quantum many-body physics, ranging from strongly-correlated quantum matter to emerging quantum technologies.

Education

Ph.D. Physics, University of Waterloo

M.Sc. Physics, Ludwig Maximilian University Munich

B.Sc. Physics, University of Florence

Selected Publications

”Integrating neural networks with a quantum simulator for state reconstruction”
G. Torlai, B. Timar, E.P.L van Nieuwenburg, H. Levine, A. Omran, A. Keesling, H. Bernien, M. Greiner, V. Vuletić, M.D. Lukin, R.G. Melko and M. Endres
Physical Review Letters 123 , 230504 (2019)

”Neural-network quantum state tomography”
G. Torlai, G. Mazzola, J. Carrasquilla, M. Troyer, R.G. Melko and G. Carleo
Nature Physics 14, 447 (2018)

”Latent space purification via neural density operators”
G. Torlai and R.G. Melko
Physical Review Letters 120, 240503 (2018)

”Neural decoder for topological codes”
G. Torlai and R.G. Melko
Physical Review Letters 119, 030501 (2017)

”Learning thermodynamics with Boltzmann machines”
G. Torlai and R.G. Melko
Physical Review B 94, 165134 (2016)

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