| Hyper-optimized tensor network contraction J Gray, S Kourtis Quantum 5, 410, 2021 | 186 | 2021 |
| 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 | 134 | 2023 |
| Machine-Learning-Assisted Many-Body Entanglement Measurement J Gray, L Banchi, A Bayat, S Bose Physical Review Letters 121 (15), 150503, 2018 | 114 | 2018 |
| quimb: A python package for quantum information and many-body calculations J Gray Journal of Open Source Software 3 (29), 819, 2018 | 100 | 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 | 91 | 2018 |
| Variational Power of Quantum Circuit Tensor Networks R Haghshenas, J Gray, AC Potter, GKL Chan Physical Review X 12 (1), 011047, 2022 | 75 | 2022 |
| Many-body localization transition: Schmidt gap, entanglement length, and scaling J Gray, S Bose, A Bayat Physical Review B 97 (20), 201105, 2018 | 69 | 2018 |
| Efficient Quantum State Sample Tomography with Basis-Dependent Neural Networks AWR Smith, J Gray, MS Kim PRX Quantum 2 (2), 020348, 2021 | 19 | 2021 |
| 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 | 17 | 2024 |
| Hyper-optimized approximate contraction of tensor networks with arbitrary geometry J Gray, GK Chan arXiv preprint arXiv:2206.07044, 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 | 12 | 2016 |
| Scale Invariant Entanglement Negativity at the Many-Body Localization Transition J Gray, A Bayat, A Pal, S Bose arXiv preprint arXiv:1908.02761, 2019 | 11 | 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 | 7 | 2022 |
| Fast Computation of Many-Body Entanglement J Gray arXiv preprint arXiv:1809.01685, 2018 | 7 | 2018 |
| Efficient Approximate Quantum State Tomography with Basis Dependent Neural-Networks AWR Smith, J Gray, MS Kim arXiv e-prints, arXiv: 2009.07601, 2020 | 3 | 2020 |
| One-step replica symmetry breaking in the language of tensor networks N Pancotti, J Gray arXiv preprint arXiv:2306.15004, 2023 | 2 | 2023 |
| Arithmetic circuit tensor networks, multivariable function representation, and high-dimensional integration R Peng, J Gray, GKL Chan Physical Review Research 5 (1), 013156, 2023 | 2 | 2023 |
| Quantum Delocalized Interactions AJ Paige, H Kwon, S Simsek, CN Self, J Gray, MS Kim Physical Review Letters 125 (24), 240406, 2020 | 2 | 2020 |
| 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, GK Chan arXiv preprint arXiv:2405.03797, 2024 | | 2024 |
| Many-Body Entanglement in Classical & Quantum Simulators J Gray UCL (University College London), 2019 | | 2019 |
Garnet Kin-Lic ChanDivision of Chemistry and Chemical Engineering, California Institute of TechnologyVerified email at caltech.edu
