Solving frustrated quantum many-particle models with convolutional neural networks X Liang, WY Liu, PZ Lin, GC Guo, YS Zhang, L He Physical Review B 98 (10), 104426, 2018 | 116 | 2018 |
Hybrid convolutional neural network and projected entangled pair states wave functions for quantum many-particle states X Liang, SJ Dong, L He Physical Review B 103 (3), 035138, 2021 | 38 | 2021 |
Bridging the gap between deep learning and frustrated quantum spin system for extreme-scale simulations on new generation of Sunway supercomputer M Li, J Chen, Q Xiao, F Wang, Q Jiang, X Zhao, R Lin, H An, X Liang, L He IEEE Transactions on Parallel and Distributed Systems 33 (11), 2846-2859, 2022 | 23 | 2022 |
Generation of Bose-Einstein condensates’ ground state through machine learning X Liang, H Zhang, S Liu, Y Li, YS Zhang Scientific reports 8 (1), 16337, 2018 | 14 | 2018 |
Ai for quantum mechanics: High performance quantum many-body simulations via deep learning X Zhao, M Li, Q Xiao, J Chen, F Wang, L Shen, M Zhao, W Wu, H An, L He, ... SC22: International Conference for High Performance Computing, Networking …, 2022 | 11 | 2022 |
Deep learning representations for quantum many-body systems on heterogeneous hardware X Liang, M Li, Q Xiao, J Chen, C Yang, H An, L He Machine Learning: Science and Technology 4 (1), 015035, 2023 | 7 | 2023 |
Exponentially Complex Quantum Many-Body Simulation via Scalable Deep Learning Method X Liang, M Li, Q Xiao, H An, L He, X Zhao, J Chen, C Yang, F Wang, ... arXiv preprint arXiv:2204.07816, 2022 | 6 | 2022 |
Automatic BLAS Offloading on Unified Memory Architecture: A Study on NVIDIA Grace-Hopper J Li, Y Wang, X Liang, H Liu Practice and Experience in Advanced Research Computing 2024: Human Powered …, 2024 | 4 | 2024 |
Solving Fermi-Hubbard-type models by tensor representations of backflow corrections YT Zhou, ZW Zhou, X Liang Physical Review B 109 (24), 245107, 2024 | 2 | 2024 |
The quantum cocktail party problem X Liang, YD Wu, H Zhai arXiv preprint arXiv:1904.06411, 2019 | 2 | 2019 |
High Performance GPU Accelerated MuST Software X Liang, E Hanna, D Simmel, H Liu, Y Wang arXiv preprint arXiv:2308.16317, 2023 | | 2023 |