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Giacomo Torlai
Giacomo Torlai
AWS Center for Quantum Computing
Verified email at amazon.com
Title
Cited by
Cited by
Year
Neural-network quantum state tomography
G Torlai, G Mazzola, J Carrasquilla, M Troyer, R Melko, G Carleo
Nature Physics 14 (5), 447, 2018
5912018
Learning thermodynamics with Boltzmann machines
G Torlai, RG Melko
Physical Review B 94 (16), 165134, 2016
2472016
Reconstructing quantum states with generative models
J Carrasquilla, G Torlai, RG Melko, L Aolita
Nature Machine Intelligence 1 (3), 155-161, 2019
1902019
Neural decoder for topological codes
G Torlai, RG Melko
Physical review letters 119 (3), 030501, 2017
1582017
Latent space purification via neural density operators
G Torlai, RG Melko
Physical review letters 120 (24), 240503, 2018
1052018
Integrating neural networks with a quantum simulator for state reconstruction
G Torlai, B Timar, EPL Van Nieuwenburg, H Levine, A Omran, A Keesling, ...
Physical review letters 123 (23), 230504, 2019
902019
NetKet: a machine learning toolkit for many-body quantum systems
G Carleo, K Choo, D Hofmann, JET Smith, T Westerhout, F Alet, EJ Davis, ...
SoftwareX 10, 100311, 2019
682019
Machine-Learning Quantum States in the NISQ Era
G Torlai, RG Melko
Annual Review of Condensed Matter Physics 11, 325-344, 2020
582020
Precise measurement of quantum observables with neural-network estimators
G Torlai, G Mazzola, G Carleo, A Mezzacapo
Physical Review Research 2 (2), 022060, 2020
492020
Dynamics of the entanglement spectrum in spin chains
G Torlai, L Tagliacozzo, G De Chiara
Journal of Statistical Mechanics: Theory and Experiment 2014 (06), P06001, 2014
472014
Provably efficient machine learning for quantum many-body problems
HY Huang, R Kueng, G Torlai, VV Albert, J Preskill
arXiv preprint arXiv:2106.12627, 2021
392021
Learnability scaling of quantum states: Restricted Boltzmann machines
D Sehayek, A Golubeva, MS Albergo, B Kulchytskyy, G Torlai, RG Melko
Physical Review B 100 (19), 195125, 2019
322019
Quantum process tomography with unsupervised learning and tensor networks
G Torlai, CJ Wood, A Acharya, G Carleo, J Carrasquilla, L Aolita
arXiv preprint arXiv:2006.02424, 2020
282020
Wave-function positivization via automatic differentiation
G Torlai, J Carrasquilla, MT Fishman, RG Melko, MPA Fisher
Physical Review Research 2 (3), 032060, 2020
272020
QuCumber: wavefunction reconstruction with neural networks
MJS Beach, I De Vlugt, A Golubeva, P Huembeli, B Kulchytskyy, X Luo, ...
SciPost Physics 7 (1), 009, 2019
242019
How to use neural networks to investigate quantum many-body physics
J Carrasquilla, G Torlai
PRX Quantum 2 (4), 040201, 2021
16*2021
Violation of Bell's inequalities with preamplified homodyne detection
G Torlai, G McKeown, P Marek, R Filip, H Jeong, M Paternostro, ...
Physical Review A 87 (5), 052112, 2013
112013
Simulating a measurement-induced phase transition for trapped-ion circuits
S Czischek, G Torlai, S Ray, R Islam, RG Melko
Physical Review A 104 (6), 062405, 2021
92021
Augmenting quantum mechanics with artificial intelligence
G Torlai
University of Waterloo, 2018
42018
Schmidt gap in random spin chains
G Torlai, KD McAlpine, G De Chiara
Physical Review B 98 (8), 085153, 2018
42018
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Articles 1–20