| Backflow Transformations via Neural Networks for Quantum Many-Body Wave Functions D Luo, BK Clark Physical Review Letters 122 (22), 226401, 2019 | 140 | 2019 |
| Deep Learning Enabled Strain Mapping of Single-Atom Defects in Two-Dimensional Transition Metal Dichalcogenides with Sub-Picometer Precision CH Lee, A Khan, D Luo, TP Santos, C Shi, BE Janicek, S Kang, W Zhu, ... Nano Letters 20 (5), 3369-3377, 2020 | 94 | 2020 |
| 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 | 63 | 2022 |
| Probabilistic simulation of quantum circuits with the Transformer J Carrasquilla, D Luo, F Pérez, A Milsted, BK Clark, M Volkovs, L Aolita Physical Review A 104 (3), 032610, 2021 | 51* | 2021 |
| Gauge-invariant and anyonic-symmetric autoregressive neural network for quantum lattice models D Luo, Z Chen, K Hu, Z Zhao, VM Hur, BK Clark Physical Review Research 5 (1), 013216, 2023 | 50 | 2023 |
| Framework for simulating gauge theories with dipolar spin systems D Luo, J Shen, M Highman, BK Clark, B DeMarco, AX El-Khadra, ... Physical Review A 102 (3), 032617, 2020 | 48* | 2020 |
| Gauge equivariant neural networks for quantum lattice gauge theories D Luo, G Carleo, BK Clark, J Stokes Physical Review Letters 127 (27), 276402, 2021 | 41 | 2021 |
| Classical shadows for quantum process tomography on near-term quantum computers R Levy, D Luo, BK Clark Physical Review Research 6 (1), 013029, 2024 | 36 | 2024 |
| Protocol Discovery for the Quantum Control of Majoranas by Differentiable Programming and Natural Evolution Strategies L Coopmans, D Luo, G Kells, BK Clark, J Carrasquilla PRX Quantum 2 (2), 020332, 2021 | 36 | 2021 |
| Beyond many-body localized states in a spin-disordered Hubbard model X Yu, D Luo, BK Clark Physical Review B 98 (11), 115106, 2018 | 18 | 2018 |
| Finite-volume pionless effective field theory for few-nucleon systems with differentiable programming X Sun, W Detmold, D Luo, PE Shanahan Physical Review D 105 (7), 074508, 2022 | 11 | 2022 |
| Gauge Equivariant Neural Networks for 2+ 1D U (1) Gauge Theory Simulations in Hamiltonian Formulation D Luo, S Yuan, J Stokes, BK Clark NeurIPS 2022 AI for Science Workshop, 2022 | 10 | 2022 |
| Spacetime Neural Network for High Dimensional Quantum Dynamics J Wang, Z Chen, D Luo, Z Zhao, VM Hur, BK Clark 38th International Conference on Machine Learning Workshop on ”Beyond first …, 2021 | 10 | 2021 |
| Variational Neural-Network Ansatz for Continuum Quantum Field Theory JM Martyn, K Najafi, D Luo Physical Review Letters 131 (8), 081601, 2023 | 8 | 2023 |
| ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation Z Chen, L Newhouse, E Chen, D Luo, M Soljacic Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 8* | 2023 |
| Simulating 2+ 1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctions Z Chen, D Luo, K Hu, BK Clark arXiv preprint arXiv:2212.06835, 2022 | 8 | 2022 |
| Infinite Neural Network Quantum States: Entanglement and Training Dynamics D Luo, J Halverson Machine Learning: Science and Technology, 2023 | 7* | 2023 |
| Simulating quantum mechanics with a -term and an ’t Hooft anomaly on a synthetic dimension J Shen, D Luo, C Huang, BK Clark, AX El-Khadra, B Gadway, P Draper Physical Review D 105 (7), 074505, 2022 | 7 | 2022 |
| GenPhys: From Physical Processes to Generative Models Z Liu, D Luo, Y Xu, T Jaakkola, M Tegmark arXiv preprint arXiv:2304.02637, 2023 | 6 | 2023 |
| QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning D Luo, J Shen, R Dangovski, M Soljacic Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 5* | 2023 |
