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Neural-network quantum states (NQSs), variationally optimized by combining traditional methods and deep learning techniques, is a new way to find quantum many-body ground states and gradually becomes a competitor of traditional variational methods.
W. Marshall, Antiferromagnetism, Proc. R. Soc. Lond. A 232
1955
Earlier work this paper cites.
K. Hornik, M. Stinchcombe, and H. White, Multilayer feedforward networks are universal approximators, Neural Networks 2
1989
Earlier work this paper cites.
G. V. Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems 2
1989
Earlier work this paper cites.
A. W. Sandvik, Finite-size scaling of the ground-state parameters of the two-dimensional heisenberg model, Phys. Rev. B 56
1997
Earlier work this paper cites.
A. Voigt, J. Richter, and N. B. Ivanov, Marshall-peierls sign rule for excited states of the frustrated j1-j2 heisenberg antiferromagnet, Physica A: Statistical Mechanics and its Applications 245
1997
Earlier work this paper cites.
H. W. J. Blöte and Y. Deng, Cluster monte carlo simulation of the transverse ising model, Phys. Rev. E 66
2002
Earlier work this paper cites.
J. Lou and A. W. Sandvik, Variational ground states of two-dimensional antiferromagnets in the valence bond basis, Phys. Rev. B 76
2007
Earlier work this paper cites.
A. W. Sandvik and G. Vidal, Variational quantum monte carlo simulations with tensor-network states, Phys. Rev. Lett. 99
2007
Earlier work this paper cites.
S. Sorella, M. Casula, and D. Rocca, Weak binding between two aromatic rings: Feeling the van der waals attraction by quantum monte carlo methods, JOURNAL OF CHEMICAL PHYSICS 127
2007
Earlier work this paper cites.
L. Tagliacozzo, G. Evenbly, and G. Vidal, Simulation of two-dimensional quantum systems using a tree tensor network that exploits the entropic area law, Phys. Rev. B 80
2009
Earlier work this paper cites.
A. W. Sandvik and H. G. Evertz, Loop updates for variational and projector quantum monte carlo simulations in the valence-bond basis, Phys. Rev. B 82
2010
Earlier work this paper cites.
E. Stoudenmire and S. R. White, Studying two-dimensional systems with the density matrix renormalization group, Annual Review of Condensed Matter Physics 3
2012
Earlier work this paper cites.
W.-J. Hu, F. Becca, A. Parola, and S. Sorella, Direct evidence for a gapless Z 2 {Z}_{2} spin liquid by frustrating néel antiferromagnetism, Phys. Rev. B 88
2013
Earlier work this paper cites.
S.-S. Gong, W. Zhu, D. N. Sheng, O. I. Motrunich, and M. P. A. Fisher, Plaquette ordered phase and quantum phase diagram in the spin- 1 2 \frac{1}{2} J 1 − J 2 {J}_{1}\text{$-$}{J}_{2} square heisenberg model, Phys. Rev. Lett. 113
2014
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, in Proceedings of the IEEE International Conference on Computer Vision (IEEE, Piscataway, 2015) pp. 1026–1034
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, New York, 2016) pp. 770–778
2016
Earlier work this paper cites.
C. U. Press, Introduction to many-body physics, Physics Today 70
2017
Earlier work this paper cites.
G. Carleo and M. Troyer, Solving the quantum many-body problem with artificial neural networks, Science 355
2017
Earlier work this paper cites.
Y. Nomura, A. S. Darmawan, Y. Yamaji, and M. Imada, Restricted boltzmann machine learning for solving strongly correlated quantum systems, Phys. Rev. B 96
2017
Cited alongside, same era.
S. Ruder, An overview of gradient descent optimization algorithms, arXiv:1609.04747 (2017)
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, Attention is all you need, Adv. Neural Inf. Process. Syst. 30
2017
Cited alongside, same era.
D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, arXiv:1412.6980 (2017)
2017
Cited alongside, same era.
X. Liang, W.-Y. Liu, P.-Z. Lin, G.-C. Guo, Y.-S. Zhang, and L. He, Solving frustrated quantum many-particle models with convolutional neural networks, Phys. Rev. B 98
S.-H. Lin and F. Pollmann, Scaling of neural-network quantum states for time evolution, physica status solidi (b) 259
2022
Later among the works it cites.
2022
Later among the works it cites.
C.-Y. Park and M. J. Kastoryano, Expressive power of complex-valued restricted boltzmann machines for solving nonstoquastic hamiltonians, Phys. Rev. B 106
2022
Later among the works it cites.
A. Chen, K. Choo, N. Astrakhantsev, and T. Neupert, Neural network evolution strategy for solving quantum sign structures, Phys. Rev. Res. 4
2022
Later among the works it cites.
X. Qian and M. Qin, From tree tensor network to multiscale entanglement renormalization ansatz, Phys. Rev. B 105
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2018
Cited alongside, same era.
L. Wang and A. W. Sandvik, Critical level crossings and gapless spin liquid in the square-lattice spin- 1 / 2 1/2 J 1 − J 2 {J}_{1}-{J}_{2} heisenberg antiferromagnet, Phys. Rev. Lett. 121
2018
Cited alongside, same era.
G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto, and L. Zdeborová, Machine learning and the physical sciences, Rev. Mod. Phys. 91
2019
Cited alongside, same era.
K. Choo, T. Neupert, and G. Carleo, Two-dimensional frustrated J 1 − J 2 {J}_{1}\text{$-$}{J}_{2} model studied with neural network quantum states, Phys. Rev. B 100
2019
Cited alongside, same era.
M. Hibat-Allah, M. Ganahl, L. E. Hayward, R. G. Melko, and J. Carrasquilla, Recurrent neural network wave functions, Phys. Rev. Res. 2
2020
Cited alongside, same era.
A. Szabó and C. Castelnovo, Neural network wave functions and the sign problem, Phys. Rev. Res. 2
2020
Cited alongside, same era.
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. Res. 2
2020
Cited alongside, same era.
T. Westerhout, N. Astrakhantsev, K. S. Tikhonov, M. I. Katsnelson, and A. A. Bagrov, Generalization properties of neural network approximations to frustrated magnet ground states, Nat. Commun. 11
2020
Cited alongside, same era.
2022
Later among the works it cites.
W.-Y. Liu, S.-S. Gong, Y.-B. Li, D. Poilblanc, W.-Q. Chen, and Z.-C. Gu, Gapless quantum spin liquid and global phase diagram of the spin-1/2 j 1 j_{1} - j 2 j_{2} square antiferromagnetic heisenberg model, Science Bulletin 67
2022
Later among the works it cites.
R. Lam, A. Sanchez-Gonzalez, M. Willson, P. Wirnsberger, M. Fortunato, F. Alet, S. Ravuri, T. Ewalds, Z. Eaton-Rosen, W. Hu, A. Merose, S. Hoyer, G. Holland, O. Vinyals, J. Stott, A. Pritzel, S. Mohamed, and P. Battaglia, Learning skillful medium-range global weather forecasting, Science 382
2023
Closest in time.
C. Roth, A. Szabó, and A. H. MacDonald, High-accuracy variational monte carlo for frustrated magnets with deep neural networks, Phys. Rev. B 108
2023
Closest in time.
L. L. Viteritti, R. Rende, and F. Becca, Transformer variational wave functions for frustrated quantum spin systems, Phys. Rev. Lett. 130
2023
Closest in time.
Y.-H. Zhang and M. Di Ventra, Transformer quantum state: A multipurpose model for quantum many-body problems, Phys. Rev. B 107
2023
Closest in time.
M. Reh, M. Schmitt, and M. Gärttner, Optimizing design choices for neural quantum states, Phys. Rev. B 107
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
T. Westerhout, M. I. Katsnelson, and A. A. Bagrov, Many-body quantum sign structures as non-glassy ising models, Commun. Phys. 6
2023
Closest in time.
X. Qian and M. Qin, Augmenting density matrix renormalization group with disentanglers, Chinese Physics Letters 40
2023
Closest in time.
2023
Closest in time.
C. Fu, X. Zhang, H. Zhang, H. Ling, S. Xu, and S. Ji, Lattice convolutional networks for learning ground states of quantum many-body systems, in Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) (SIAM, 2024) pp. 490–498
2024
Closest in time.