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Quantum many-body physics simulation has important impacts on understanding fundamental science and has applications to quantum materials design and quantum technology.
Antiferromagnetism
Marshall, W · 1955
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Density matrix formulation for quantum renormalization groups
White, S. R · 1992
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Efficient classical simulation of slightly entangled quantum computations
Vidal, G · 2003
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Ising transition in the two-dimensional quantum j 1- j 2 heisenberg model
Capriotti, L., Fubini, A., Roscilde, T., and Tognetti, V · 2004
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Variance reduction techniques for gradient estimates in reinforcement learning
Greensmith, E., Bartlett, P. L., and Baxter, J · 2004
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Renormalization algorithms for quantum-many body systems in two and higher dimensions, 2004
Verstraete, F. and Cirac, J. I · 2004
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Efficient simulation of one-dimensional quantum many-body systems
Vidal, G · 2004
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The ising phase in the j1-j2 heisenberg model
Lante, V. and Parola, A · 2006
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Entanglement renormalization
Vidal, G · 2007
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Tensor-train decomposition
Oseledets, I. V · 2011
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Perfect sampling with unitary tensor networks
Ferris, A. J. and Vidal, G · 2012
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Solving the quantum many-body problem with artificial neural networks
Carleo, G. and Troyer, M · 2017
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Quantum entanglement in neural network states
Deng, D.-L., Li, X., and Das Sarma, S · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Interactive supercomputing on 40,000 cores for machine learning and data analysis
Reuther, A., Kepner, J., Byun, C., Samsi, S., Arcand, W., Bestor, D., Bergeron, B., Gadepally, V., Houle, M., Hubbell, M., Jones, M., Klein, A., Milechin, L., Mullen, J., Prout, A., Rosa, A., Yee, C., and Michaleas, P · 2018
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Two-dimensional frustrated J 1 − J 2 {J}_{1}\text{$-$}{J}_{2} model studied with neural network quantum states
Choo, K., Neupert, T., and Carleo, G · 2019
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Modern condensed matter physics
Girvin, S. M. and Yang, K · 2019
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Neural-network approach to dissipative quantum many-body dynamics
Hartmann, M. J. and Carleo, G · 2019
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Deep neural network solution of the electronic schrödinger equation, 2019
Hermann, J., Schätzle, Z., and Noé, F · 2019
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Quantum many-body dynamics in two dimensions with artificial neural networks
Schmitt, M. and Heyl, M · 2020
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Deep autoregressive models for the efficient variational simulation of many-body quantum systems
Sharir, O., Levine, Y., Wies, N., Carleo, G., and Shashua, A · 2020
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Restricted boltzmann machines for quantum states with non-abelian or anyonic symmetries
Vieijra, T., Casert, C., Nys, J., De Neve, W., Haegeman, J., Ryckebusch, J., and Verstraete, F · 2020
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Neural network representation of tensor network and chiral states
Huang, Y. and Moore, J. E · 2021
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Dirac-type nodal spin liquid revealed by refined quantum many-body solver using neural-network wave function, correlation ratio, and level spectroscopy
Nomura, Y. and Imada, M · 2021
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Lu, S., Gao, X., and Duan, L.-M · 2019
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Backflow transformations via neural networks for quantum many-body wave functions
Luo, D. and Clark, B. K · 2019
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Variational quantum monte carlo method with a neural-network ansatz for open quantum systems
Nagy, A. and Savona, V · 2019
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Pytorch: An imperative style, high-performance deep learning library
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Variational neural-network ansatz for steady states in open quantum systems
Vicentini, F., Biella, A., Regnault, N., and Ciuti, C · 2019
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Constructing neural stationary states for open quantum many-body systems
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Real time evolution with neural-network quantum states, 2020
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Neural tensor contractions and the expressive power of deep neural quantum states, 2021
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Simulating 2+ 1d lattice quantum electrodynamics at finite density with neural flow wavefunctions
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Matrix product states with backflow correlations
Lami, G., Carleo, G., and Collura, M · 2022
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NetKet 3: Machine learning toolbox for many-body quantum systems
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From tensor network quantum states to tensorial recurrent neural networks
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Efficient representation of quantum many-body states with deep neural networks
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Generalization properties of neural network approximations to frustrated magnet ground states
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