| Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry S Lee, J Lee, H Zhai, Y Tong, AM Dalzell, A Kumar, P Helms, J Gray, ... Nature Communications 14 (1), 1952, 2023 | 366* | 2023 |
| Hyper-optimized tensor network contraction J Gray, S Kourtis Quantum 5, 410, 2021 | 343 | 2021 |
| quimb: A python package for quantum information and many-body calculations J Gray Journal of Open Source Software 3 (29), 819, 2018 | 194 | 2018 |
| Machine-Learning-Assisted Many-Body Entanglement Measurement J Gray, L Banchi, A Bayat, S Bose Physical Review Letters 121 (15), 150503, 2018 | 159 | 2018 |
| opt_einsum - A Python package for optimizing contraction order for einsum-like expressions DGA Smith, J Gray Journal of Open Source Software 3 (26), 753, 2018 | 156 | 2018 |
| Variational Power of Quantum Circuit Tensor Networks R Haghshenas, J Gray, AC Potter, GKL Chan Physical Review X 12 (1), 011047, 2022 | 134 | 2022 |
| Fast and converged classical simulations of evidence for the utility of quantum computing before fault tolerance T Begušić, J Gray, GKL Chan Science Advances 10 (3), eadk4321, 2024 | 126 | 2024 |
| Computational Power of Random Quantum Circuits in Arbitrary Geometries M DeCross, R Haghshenas, M Liu, E Rinaldi, J Gray, Y Alexeev, ... Physical Review X 15 (2), 021052, 2025 | 109 | 2025 |
| Many-body localization transition: Schmidt gap, entanglement length, and scaling J Gray, S Bose, A Bayat Physical Review B 97 (20), 201105, 2018 | 87 | 2018 |
| Hyper-optimized approximate contraction of tensor networks with arbitrary geometry J Gray, GKL Chan Phys. Rev. X 14 (1), 011009, 2024 | 52* | 2024 |
| Efficient Quantum State Sample Tomography with Basis-Dependent Neural Networks AWR Smith, J Gray, MS Kim PRX Quantum 2 (2), 020348, 2021 | 43 | 2021 |
| Rapid quantum ground state preparation via dissipative dynamics Y Zhan, Z Ding, J Huhn, J Gray, J Preskill, GK Chan, L Lin arXiv preprint arXiv:2503.15827, 2025 | 32 | 2025 |
| Tensor networks for quantum computing A Berezutskii, M Liu, A Acharya, R Ellerbrock, J Gray, R Haghshenas, ... Nature Reviews Physics 7 (10), 581-593, 2025 | 27 | 2025 |
| Digital quantum magnetism at the frontier of classical simulations R Haghshenas, E Chertkov, M Mills, W Kadow, SH Lin, YH Chen, C Cade, ... arXiv preprint arXiv:2503.20870, 2025 | 25 | 2025 |
| Simulating quantum dynamics in two-dimensional lattices with tensor network influence functional belief propagation G Park, J Gray, GK Chan arXiv preprint arXiv:2504.07344, 2025 | 17 | 2025 |
| Scale Invariant Entanglement Negativity at the Many-Body Localization Transition J Gray, A Bayat, A Pal, S Bose arXiv preprint arXiv:1908.02761, 2019 | 16 | 2019 |
| Using Hyperoptimized Tensor Networks and First-Principles Electronic Structure to Simulate the Experimental Properties of the Giant {Mn84} Torus DT Chen, P Helms, AR Hale, M Lee, C Li, J Gray, G Christou, VS Zapf, ... The Journal of Physical Chemistry Letters 13 (10), 2365-2370, 2022 | 13 | 2022 |
| Unravelling quantum dot array simulators via singlet-triplet measurements J Gray, A Bayat, RK Puddy, CG Smith, S Bose Physical Review B 94 (19), 195136, 2016 | 13 | 2016 |
| Tensor Network Computations That Capture Strict Variationality, Volume Law Behavior, and the Efficient Representation of Neural Network States WY Liu, SJ Du, R Peng, J Gray, GKL Chan Physical Review Letters 133 (26), 260404, 2024 | 10 | 2024 |
| Fast Computation of Many-Body Entanglement J Gray arXiv preprint arXiv:1809.01685, 2018 | 10 | 2018 |
Garnet Kin-Lic ChanDivision of Chemistry and Chemical Engineering, California Institute of TechnologyVerified email at caltech.edu
