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Artificial Neural Networks were recently shown to be an efficient representation of highly-entangled many-body quantum states.
Robert Jastrow, “Many-Body Problem with Strong Forces,” Physical Review 98
1955
Earlier work this paper cites.
W. L. McMillan, “Ground State of Liquid He4,” Physical Review 138
1965
Earlier work this paper cites.
Mark Fannes, Bruno Nachtergaele, and Reinhard F Werner, “Finitely correlated states on quantum spin chains,” Communications in mathematical physics 144
1992
Earlier work this paper cites.
Henk W. J. Blöte and Youjin Deng, “Cluster Monte Carlo simulation of the transverse Ising model,” Physical Review E 66
2002
Earlier work this paper cites.
Frank Verstraete and J Ignacio Cirac, “Renormalization algorithms for quantum-many body systems in two and higher dimensions,” arXiv preprint cond-mat/0407066 (2004)
2004
Earlier work this paper cites.
David Perez-García, Frank Verstraete, Michael M Wolf, and J Ignacio Cirac, “Matrix product state representations,” Quantum Information and Computation 7
2007
Earlier work this paper cites.
Guifré Vidal, “Class of quantum many-body states that can be efficiently simulated,” Physical review letters 101
2008
Earlier work this paper cites.
2011
Earlier work this paper cites.
Diederik Kingma and Jimmy Ba, “Adam: A method for stochastic optimization,” 3rd International Conference on Learning Representations (ICLR) (2014)
2014
Earlier work this paper cites.
Benigno Uria, Marc-Alexandre Cote, Karol Gregor, Iain Murray, and Hugo Larochelle, “Neural Autoregressive Distribution Estimation,” Journal of Machine Learning Research () 17
2016
Cited alongside, same era.
Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al. , “Conditional image generation with pixelcnn decoders,” in Advances in Neural Information Processing Systems (2016) pp. 4790–4798
2016
Cited alongside, same era.
Dong-Ling Deng, Xiaopeng Li, and S Das Sarma, “Quantum entanglement in neural network states,” Physical Review X 7
2017
Cited alongside, same era.
Hiroki Saito and Masaya Kato, “Machine Learning Technique to Find Quantum Many-Body Ground States of Bosons on a Lattice,” Journal of the Physical Society of Japan 87
2017
Cited alongside, same era.
Kenny Choo, Giuseppe Carleo, Nicolas Regnault, and Titus Neupert, “Symmetries and Many-Body Excitations with Neural-Network Quantum States,” Physical Review Letters 121
2018
Later among the works it cites.
2018
Later among the works it cites.
Giacomo Torlai, Guglielmo Mazzola, Juan Carrasquilla, Matthias Troyer, Roger Melko, and Giuseppe Carleo, “Neural-network quantum state tomography,” Nature Physics 14
2018
Later among the works it cites.
2018
Later among the works it cites.
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2017
Cited alongside, same era.
Wen-Yuan Liu, Shao-Jun Dong, Yong-Jian Han, Guang-Can Guo, and Lixin He, “Gradient optimization of finite projected entangled pair states,” Physical Review B 95
2017
Cited alongside, same era.
Jing Chen, Song Cheng, Haidong Xie, Lei Wang, and Tao Xiang, “Equivalence of restricted boltzmann machines and tensor network states,” Phys. Rev. B 97
2018
Cited alongside, same era.
Ivan Glasser, Nicola Pancotti, Moritz August, Ivan D. Rodriguez, and J. Ignacio Cirac, “Neural-network quantum states, string-bond states, and chiral topological states,” Phys. Rev. X 8
2018
Cited alongside, same era.
Raphael Kaubruegger, Lorenzo Pastori, and Jan Carl Budich, “Chiral topological phases from artificial neural networks,” Physical Review B 97
2018
Cited alongside, same era.
Giuseppe Carleo and Matthias Troyer, “Solving the quantum many-body problem with artificial neural networks,” Science 355
Cited in the paper.
FlowKet: an open-source library based on Tensorflow for running Variational Monte-Carlo simulations on GPUs, https://github.com/HUJI-Deep/FlowKet
Cited in the paper.
Giuseppe Carleo and Matthias Troyer, “Solving the quantum many-body problem with artificial neural networks,” Science 355
Cited in the paper.
L. He, H. An, C. Yang, F. Wang, J. Chen, C. Wang, W. Liang, S. Dong, Q. Sun, W. Han, W. Liu, Y. Han, and W. Yao, “Peps++: Towards extreme-scale simulations of strongly correlated quantum many-particle models on sunway taihulight,” IEEE Transactions on Parallel and Distributed Systems 29
2018
Later among the works it cites.
Yoav Levine, Or Sharir, Nadav Cohen, and Amnon Shashua, “Quantum entanglement in deep learning architectures,” Physical review letters (2019)
2019
Closest in time.
Juan Carrasquilla, Giacomo Torlai, Roger G Melko, and Leandro Aolita, “Reconstructing quantum states with generative models,” Nature Machine Intelligence 1
2019
Closest in time.