Neural-network quantum state tomography G Torlai, G Mazzola, J Carrasquilla, M Troyer, R Melko, G Carleo Nature Physics 14 (5), 447, 2018 | 892 | 2018 |
Reconstructing quantum states with generative models J Carrasquilla, G Torlai, RG Melko, L Aolita Nature Machine Intelligence 1 (3), 155-161, 2019 | 318 | 2019 |
Learning thermodynamics with Boltzmann machines G Torlai, RG Melko Physical Review B 94 (16), 165134, 2016 | 298 | 2016 |
Neural decoder for topological codes G Torlai, RG Melko Physical review letters 119 (3), 030501, 2017 | 207 | 2017 |
Provably efficient machine learning for quantum many-body problems HY Huang, R Kueng, G Torlai, VV Albert, J Preskill Science 377 (6613), eabk3333, 2022 | 200 | 2022 |
Latent space purification via neural density operators G Torlai, RG Melko Physical review letters 120 (24), 240503, 2018 | 153 | 2018 |
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 | 136 | 2019 |
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 | 129 | 2019 |
Machine-Learning Quantum States in the NISQ Era G Torlai, RG Melko Annual Review of Condensed Matter Physics 11, 325-344, 2020 | 95 | 2020 |
Precise measurement of quantum observables with neural-network estimators G Torlai, G Mazzola, G Carleo, A Mezzacapo Physical Review Research 2 (2), 022060, 2020 | 81 | 2020 |
Quantum process tomography with unsupervised learning and tensor networks G Torlai, CJ Wood, A Acharya, G Carleo, J Carrasquilla, L Aolita Nature Communications 14 (1), 2858, 2023 | 75 | 2023 |
How to use neural networks to investigate quantum many-body physics J Carrasquilla, G Torlai PRX Quantum 2 (4), 040201, 2021 | 64* | 2021 |
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 | 51 | 2014 |
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 | 50 | 2019 |
Wave-function positivization via automatic differentiation G Torlai, J Carrasquilla, MT Fishman, RG Melko, MPA Fisher Physical Review Research 2 (3), 032060, 2020 | 41 | 2020 |
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 | 39 | 2019 |
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 | 28 | 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 | 13 | 2013 |
Augmenting quantum mechanics with artificial intelligence G Torlai University of Waterloo, 2018 | 7 | 2018 |
Schmidt gap in random spin chains G Torlai, KD McAlpine, G De Chiara Physical Review B 98 (8), 085153, 2018 | 7 | 2018 |