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We develop a pairing-based graph neural network for simulating quantum many-body systems.
1909
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1912
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D.-L. Deng, X. Li, and S. Das Sarma, Quantum entanglement in neural network states, Phys. Rev. X 7
2017
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X. Gao and L.-M. Duan, Efficient representation of quantum many-body states with deep neural networks, Nature Communications 8
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2017
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2018
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J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang, JAX: composable transformations of Python+NumPy programs (2018)
2018
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Y. Levine, O. Sharir, N. Cohen, and A. Shashua, Quantum entanglement in deep learning architectures, Physical Review Letters 122
2019
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K. Choo, T. Neupert, and G. Carleo, Two-dimensional frustrated j 1- j 2 model studied with neural network quantum states, Physical Review B 100
2019
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D. Luo and B. K. Clark, Backflow transformations via neural networks for quantum many-body wave functions, Physical Review Letters 122
2019
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F. Vicentini, A. Biella, N. Regnault, and C. Ciuti, Variational neural-network ansatz for steady states in open quantum systems, Physical Review Letters 122
2019
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N. Yoshioka and R. Hamazaki, Constructing neural stationary states for open quantum many-body systems, Phys. Rev. B 99
2019
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M. J. Hartmann and G. Carleo, Neural-network approach to dissipative quantum many-body dynamics, Phys. Rev. Lett. 122
2019
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A. Nagy and V. Savona, Variational quantum monte carlo method with a neural-network ansatz for open quantum systems, Phys. Rev. Lett. 122
2019
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O. Sharir, Y. Levine, N. Wies, G. Carleo, and A. Shashua, Deep autoregressive models for the efficient variational simulation of many-body quantum systems, Phys. Rev. Lett. 124
2020
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N. Irikura and H. Saito, Neural-network quantum states at finite temperature, Physical Review Research 2
2020
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X. Han and S. A. Hartnoll, Deep quantum geometry of matrices, Physical Review X 10
2020
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J. Robledo Moreno, G. Carleo, A. Georges, and J. Stokes, Fermionic wave functions from neural-network constrained hidden states, Proceedings of the National Academy of Sciences 119
2022
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2022
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2022
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2022
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D. Pfau, J. S. Spencer, A. G. D. G. Matthews, and W. M. C. Foulkes, Ab initio solution of the many-electron schrödinger equation with deep neural networks, Phys. Rev. Research 2
2020
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J. Stokes, J. R. Moreno, E. A. Pnevmatikakis, and G. Carleo, Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum states, Physical Review B 102
2020
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M. Schmitt and M. Heyl, Quantum many-body dynamics in two dimensions with artificial neural networks, Physical Review Letters 125
2020
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Y. Zeng and A. H. MacDonald, Electrically controlled two-dimensional electron-hole fluids, Physical Review B 102
2020
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2021
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D. Luo and J. Halverson, Infinite neural network quantum states (2021)
2021
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C. K. Lee, P. Patil, S. Zhang, and C. Y. Hsieh, Neural-network variational quantum algorithm for simulating many-body dynamics, Phys. Rev. Research 3
2021
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X. Li, Z. Li, and J. Chen, Ab initio calculation of real solids via neural network ansatz, Nature Communications 13
2022
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M. Scherbela, R. Reisenhofer, L. Gerard, P. Marquetand, and P. Grohs, Solving the electronic schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks, Nature Computational Science 2
2022
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G. Cassella, H. Sutterud, S. Azadi, N. Drummond, D. Pfau, J. S. Spencer, and W. M. C. Foulkes, Discovering quantum phase transitions with fermionic neural networks, Physical Review Letters 130
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H. Xie, Z.-H. Li, H. Wang, L. Zhang, and L. Wang, Deep variational free energy approach to dense hydrogen, Physical Review Letters 131
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