Giacomo Torlai

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Roger G. MelkoFaculty, University of Waterloo & Perimeter Institute; Affiliate, Vector InstituteVerified email at uwaterloo.ca
Juan Felipe Carrasquilla ÁlvarezETH ZürichVerified email at ethz.ch
Giuseppe CarleoAssociate Professor of Physics - EPFL, Lausanne, SwitzerlandVerified email at epfl.ch
Guglielmo MazzolaUniversity of ZurichVerified email at uzh.ch
Matthias TroyerMicrosoft QuantumVerified email at microsoft.com
Leandro AolitaQuantum Research Centre - Technology Innovation InstituteVerified email at tii.ae
Evert van NieuwenburgLeiden UniversityVerified email at lorentz.leidenuniv.nl
Manuel EndresCaltechVerified email at caltech.edu
Gabriele De ChiaraProfessor of Quantum Information, Queen's University BelfastVerified email at qub.ac.uk
Antonio MezzacapoIBM QuantumVerified email at us.ibm.com
Alexander KeeslingCEO, QuEra Computing IncVerified email at quera.com
Harry LevineQuantum Research Scientist, AWS Center for Quantum ComputingVerified email at amazon.com
Richard KuengJohannes Kepler University Linz, AustriaVerified email at jku.at
Hsin-Yuan Huang (Robert)California Institute of Technology, Google Quantum AIVerified email at caltech.edu
Victor V. AlbertQuICS @ NIST & University of MarylandVerified email at umd.edu
John PreskillRichard P. Feynman Professor of Theoretical Physics, California Institute of TechnologyVerified email at caltech.edu
Bohdan KulchytskyyLead Scientist at 1QB Information Technologies, Inc.Verified email at uwaterloo.ca
Markus GreinerProfessor of Physics, Harvard UniversityVerified email at physics.harvard.edu
Hannes BernienAssistant Professor, University of ChicagoVerified email at uchicago.edu
Ahmed OmranSoftware Engineer, GoogleVerified email at google.com

Giacomo Torlai

AWS Center for Quantum Computing

Verified email at amazon.com

Quantum Hardware Quantum Engineering Tensor Networks Machine Learning


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Neural-network quantum state tomography G Torlai, G Mazzola, J Carrasquilla, M Troyer, R Melko, G Carleo Nature Physics 14 (5), 447, 2018	936	2018
Reconstructing quantum states with generative models J Carrasquilla, G Torlai, RG Melko, L Aolita Nature Machine Intelligence 1 (3), 155-161, 2019	336	2019
Learning thermodynamics with Boltzmann machines G Torlai, RG Melko Physical Review B 94 (16), 165134, 2016	300	2016
Provably efficient machine learning for quantum many-body problems HY Huang, R Kueng, G Torlai, VV Albert, J Preskill Science 377 (6613), eabk3333, 2022	242	2022
Neural decoder for topological codes G Torlai, RG Melko Physical review letters 119 (3), 030501, 2017	216	2017
Latent space purification via neural density operators G Torlai, RG Melko Physical review letters 120 (24), 240503, 2018	158	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	137	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	134	2019
Machine-Learning Quantum States in the NISQ Era G Torlai, RG Melko Annual Review of Condensed Matter Physics 11, 325-344, 2020	103	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	89	2023
Precise measurement of quantum observables with neural-network estimators G Torlai, G Mazzola, G Carleo, A Mezzacapo Physical Review Research 2 (2), 022060, 2020	87	2020
How to use neural networks to investigate quantum many-body physics J Carrasquilla, G Torlai PRX Quantum 2 (4), 040201, 2021	67*	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	56	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	43	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	41	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	30	2021
Demonstrating a long-coherence dual-rail erasure qubit using tunable transmons H Levine, A Haim, JSC Hung, N Alidoust, M Kalaee, L DeLorenzo, ... Physical Review X 14 (1), 011051, 2024	25	2024
Violation of Bell's inequalities with preamplified homodyne detection G Torlai, G McKeown, P Marek, R Filip, H Jeong, M Paternostro, ... Physical Review A—Atomic, Molecular, and Optical Physics 87 (5), 052112, 2013	13	2013
Augmenting quantum mechanics with artificial intelligence G Torlai University of Waterloo, 2018	8	2018

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