Juan Felipe Carrasquilla Álvarez

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Roger G. MelkoFaculty, University of Waterloo & Perimeter Institute; Affiliate, Vector InstituteVerified email at uwaterloo.ca
Giacomo TorlaiAWS Center for Quantum ComputingVerified email at amazon.com
Mohamed HIBAT-ALLAHAssistant professor, University of WaterlooVerified email at uwaterloo.ca
Giuseppe CarleoAssociate Professor of Physics - EPFL, Lausanne, SwitzerlandVerified email at epfl.ch
Marcos RigolDistinguished Professor of Physics, Penn State UniversityVerified email at phys.psu.edu
Roeland WiersemaPostdoc, Flatiron InstituteVerified email at flatironinstitute.org
Leandro AolitaQuantum Research Centre - Technology Innovation InstituteVerified email at tii.ae
Ehsan KhatamiSan Jose State UniversityVerified email at sjsu.edu
Guglielmo MazzolaUniversity of ZurichVerified email at uzh.ch
Matthias TroyerMicrosoft QuantumVerified email at microsoft.com
Federico BeccaUniversity of TriesteVerified email at units.it
Bryan ClarkAssociate Professor, University of Illinois at Urbana ChampaignVerified email at illinois.edu
Roman Eduardo Castaneda SepulvedaUniversidad Nacional de ColombiaVerified email at unal.edu.co
Di LuoMassachusetts Institute of TechnologyVerified email at mit.edu
Jorge Garcia-SucerquiaProfessor Universidad Nacional de Colombia Sede MedellinVerified email at unal.edu.co
Lauren E. HaywardTeaching Faculty, Perimeter Institute; Adjunct Assistant Professor, University of WaterlooVerified email at perimeterinstitute.ca
Zhuo ChenMassachusetts Institute of TechnologyVerified email at mit.edu
Cunlu ZhouAssistant Professor, Université de SherbrookeVerified email at usherbrooke.ca
Estelle InackPerimeter InstituteVerified email at pitp.ca
Simon TrebstUniversity of CologneVerified email at thp.uni-koeln.de

Juan Felipe Carrasquilla Álvarez

ETH Zürich

Verified email at ethz.ch

Theoretical condensed matter physics quantum computing machine learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Machine learning phases of matter J Carrasquilla, RG Melko Nature Physics 13 (5), 431, 2017	1694	2017
Neural-network quantum state tomography G Torlai, G Mazzola, J Carrasquilla, M Troyer, R Melko, G Carleo Nature physics 14 (5), 447-450, 2018	1017	2018
Machine learning phases of strongly correlated fermions K Ch’Ng, J Carrasquilla, RG Melko, E Khatami Physical Review X 7 (3), 031038, 2017	434	2017
Machine learning quantum phases of matter beyond the fermion sign problem P Broecker, J Carrasquilla, RG Melko, S Trebst Scientific reports 7 (1), 8823, 2017	384	2017
Observation of topological phenomena in a programmable lattice of 1,800 qubits AD King, J Carrasquilla, J Raymond, I Ozfidan, E Andriyash, A Berkley, ... Nature 560 (7719), 456-460, 2018	378	2018
Reconstructing quantum states with generative models J Carrasquilla, G Torlai, RG Melko, L Aolita Nature Machine Intelligence 1 (3), 155-161, 2019	375	2019
Recurrent neural network wave functions M Hibat-Allah, M Ganahl, LE Hayward, RG Melko, J Carrasquilla Physical Review Research 2 (2), 023358, 2020	290	2020
Machine learning for quantum matter J Carrasquilla Advances in Physics: X 5 (1), 1797528, 2020	283	2020
Exploring entanglement and optimization within the hamiltonian variational ansatz R Wiersema, C Zhou, Y de Sereville, JF Carrasquilla, YB Kim, H Yuen PRX quantum 1 (2), 020319, 2020	241	2020
Restricted Boltzmann machines in quantum physics RG Melko, G Carleo, J Carrasquilla, JI Cirac Nature Physics 15 (9), 887-892, 2019	234	2019
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	112	2023
Scaling of the gap, fidelity susceptibility, and Bloch oscillations across the superfluid-to-Mott-insulator transition in the one-dimensional Bose-Hubbard model J Carrasquilla, SR Manmana, M Rigol Physical Review A—Atomic, Molecular, and Optical Physics 87 (4), 043606, 2013	103	2013
Neural Gutzwiller-projected variational wave functions F Ferrari, F Becca, J Carrasquilla Physical Review B 100 (12), 125131, 2019	94	2019
Attention-based quantum tomography P Cha, P Ginsparg, F Wu, J Carrasquilla, PL McMahon, EA Kim Machine Learning: Science and Technology 3 (1), 01LT01, 2021	93	2021
Autoregressive neural network for simulating open quantum systems via a probabilistic formulation D Luo, Z Chen, J Carrasquilla, BK Clark Physical review letters 128 (9), 090501, 2022	92	2022
Variational neural annealing M Hibat-Allah, EM Inack, R Wiersema, RG Melko, J Carrasquilla Nature Machine Intelligence 3 (11), 952-961, 2021	91	2021
Phase diagram of the half-filled one-dimensional -- model T Mishra, J Carrasquilla, M Rigol Physical Review B—Condensed Matter and Materials Physics 84 (11), 115135, 2011	87	2011
Modern applications of machine learning in quantum sciences A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ... arXiv preprint arXiv:2204.04198, 2022	84	2022
How to use neural networks to investigate quantum many-body physics J Carrasquilla, G Torlai PRX Quantum 2 (4), 040201, 2021	82*	2021
Spectral tensor networks for many-body localization A Chandran, J Carrasquilla, IH Kim, DA Abanin, G Vidal Physical Review B 92 (2), 024201, 2015	71	2015

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