Approximating quantum many-body wave-functions using artificial neural networks

A. N. Kolmogorov, “On the representation of continuous functions of several variables by superpositions of continuous functions of a smaller number of variables,” Doklady Akademii Nauk SSSR 108

1961

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. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of control, signals and systems 2

1989

Earlier work this paper cites.

Anders W. Sandvik and Juhani Kurkijärvi, “Quantum monte carlo simulation method for spin systems,” Phys. Rev. B 43

1991

Earlier work this paper cites.

Steven R. White, “Density matrix formulation for quantum renormalization groups,” Phys. Rev. Lett. 69

1992

Earlier work this paper cites.

N. V. Prokof’ev, B. V. Svistunov, and I. S. Tupitsyn, Phys. Lett. A 238

1998

Earlier work this paper cites.

Stefano Curtarolo, Dane Morgan, Kristin Persson, John Rodgers, and Gerbrand Ceder, “Predicting crystal structures with data mining of quantum calculations,” Phys. Rev. Lett. 91

2003

Earlier work this paper cites.

F. Verstraete, V. Murg, and J.I. Cirac, “Matrix product states, projected entangled pair states, and variational renormalization group methods for quantum spin systems,” Advances in Physics 57

2008

Earlier work this paper cites.

Ulrich Schollwoeck, “The density-matrix renormalization group in the age of matrix product states,” Annals of Physics 326

2011

Earlier work this paper cites.

P. Mehta and D. J. Schwab, “An exact mapping between the Variational Renormalization Group and Deep Learning,” ArXiv e-prints (2014), arXiv:1410.3831 [stat.ML]

Original

2014

Earlier work this paper cites.

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” ArXiv e-prints (2014), arXiv:1412.6980 [cs.LG]

Original

2014

Earlier work this paper cites.

M. A. Nielsen, Neural Networks and Deep Learning ( Determination Press, 2015)

2015

Earlier work this paper cites.

S. V. Kalinin, B. G. Sumpter, and R. K. Archibald, “Big¨cdeep¨csmart data in imaging for guiding materials design,” Nat Mater 14

2015

Earlier work this paper cites.

Luca M. Ghiringhelli, Jan Vybiral, Sergey V. Levchenko, Claudia Draxl, and Matthias Scheffler, “Big data of materials science: Critical role of the descriptor,” Phys. Rev. Lett. 114

2015

Earlier work this paper cites.

M. Abadi, Large-scale machine learning on heterogeneous systems (Software available from tensorflow.org, 2015)

2015

Cited alongside, same era.

J. Gubernatis, N. Kawashima, and P. Werner, Quantum Monte Carlo Methods: Algorithms for Lattice Models ( Cambridge University Press, Cambridge, 2016)

2016

Cited alongside, same era.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning ( book in preparation for MIT Press, 2016)

2016

Cited alongside, same era.

E. Miles Stoudenmire and David J. Schwab, Advances in Neural Information Processing Systems 29

2016

Cited alongside, same era.

Lei Wang, “Discovering phase transitions with unsupervised learning,” Phys. Rev. B 94

2016

Cited alongside, same era.

Yi Zhang and Eun-Ah Kim, “Quantum loop topography for machine learning,” Phys. Rev. Lett. 118

2017

Closest in time.

Kelvin Ch’ng, Juan Carrasquilla, Roger G. Melko, and Ehsan Khatami, “Machine learning phases of strongly correlated fermions,” Phys. Rev. X 7

2017

Closest in time.

P. Ponte and R. G. Melko, “Kernel methods for interpretable machine learning of order parameters,” ArXiv e-prints (2017), arXiv:1704.05848 [cond-mat.stat-mech]

Original

2017

Closest in time.

P. Broecker, J. Carrasquilla, R. G. Melko, and S. Trebst, “Machine learning quantum phases of matter beyond the fermion sign problem,” Scientific Reports 7

2017

Closest in time.

Li Huang, Yi-feng Yang, and Lei Wang, “Recommender engine for continuous-time quantum monte carlo methods,” Phys. Rev. E 95

2017

Closest in time.