01. Cichocki A, Phan A-H, Zhao Q, Lee N, Oseledets IV, Sugiyama M, et al. Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives. Foundations and Trends® in Machine Learning . 2017;9(6):249–429. http://arxiv.org/abs/1708.09165
02. Chen J, Cheng S, Xie H, Wang L, Xiang T. On the Equivalence of Restricted Boltzmann Machines and Tensor Network States. arXiv:170104831 [cond-mat, physics:quant-ph, stat] . 2017 Jan 17; http://arxiv.org/abs/1701.04831
03. Gao X, Duan L-M. Efficient Representation of Quantum Many-body States with Deep Neural Networks. arXiv:170105039 [cond-mat, physics:quant-ph] . 2017 Jan 18 ; http://arxiv.org/abs/1701.05039
04. Huang Y, Moore JE. Neural network representation of tensor network and chiral states. arXiv:170106246 [cond-mat] . 2017 Jan 22;http://arxiv.org/abs/1701.06246
05. Mills K, Spanner M, Tamblyn I. Deep learning and the Schr\"odinger equation. arXiv:170201361 [cond-mat, physics:physics] . 2017 Feb 4 ;http://arxiv.org/abs/1702.01361
06. Wetzel SJ. Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders. arXiv:170302435 [cond-mat, stat] . 2017 Mar 7 ; http://arxiv.org/abs/1703.02435
07. Levine Y, Yakira D, Cohen N, Shashua A. Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design. arXiv:170401552 [quant-ph] . 2017 Apr 5; http://arxiv.org/abs/1704.01552
08. Schindler F, Regnault N, Neupert T. Probing many-body localization with neural networks. arXiv:170401578 [cond-mat] . 2017 Apr 5 ;http://arxiv.org/abs/1704.01578
09. Koch-Janusz M, Ringel Z. Mutual Information, Neural Networks and the Renormalization Group. arXiv:170406279 [cond-mat] . 2017 Apr 20;http://arxiv.org/abs/1704.06279
10. Bradde S, Bialek W. PCA meets RG. Journal of Statistical Physics . 2017 May;167(3–4):462–75. http://arxiv.org/abs/1610.09733
11. Lu S, Huang S, Li K, Li J, Chen J, Lu D, et al. A Separability-Entanglement Classifier via Machine Learning. arXiv:170501523 [quant-ph] . 2017 May 3 ;http://arxiv.org/abs/1705.01523
12. Cohen N, Sharir O, Levine Y, Tamari R, Yakira D, Shashua A. Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions. arXiv:170502302 [cs] . 2017 May 5; http://arxiv.org/abs/1705.02302
13. Deng D-L, Li X, Sarma SD. Quantum Entanglement in Neural Network States. Physical Review X . 2017 May 11;7(2). http://arxiv.org/abs/1701.04844
14. Rolnick D, Tegmark M. The power of deeper networks for expressing natural functions. arXiv:170505502 [cs, stat] . 2017 May 15 ;http://arxiv.org/abs/1705.05502
15. Cristoforetti M, Jurman G, Nardelli AI, Furlanello C. Towards meaningful physics from generative models. arXiv:170509524 [cond-mat, physics:hep-lat] . 2017 May 26 ; http://arxiv.org/abs/1705.09524
16. Oprisa D, Toth P. Criticality & Deep Learning II: Momentum Renormalisation Group. arXiv:170511023 [cond-mat] . 2017 May 31;http://arxiv.org/abs/1705.11023
17. Broecker P, Assaad FF, Trebst S. Quantum phase recognition via unsupervised machine learning. arXiv:170700663 [cond-mat] . 2017 Jul 3 ;http://arxiv.org/abs/1707.00663
18. Gallego AJ, Orus R. The physical structure of grammatical correlations: equivalences, formalizations and consequences. arXiv:170801525 [cond-mat, physics:physics, physics:quant-ph] . 2017 Aug 4; http://arxiv.org/abs/1708.01525
19. Morningstar A, Melko RG. Deep Learning the Ising Model Near Criticality. arXiv:170804622 [cond-mat, stat] . 2017 Aug 15 ;http://arxiv.org/abs/1708.04622
20. You Y-Z, Yang Z, Qi X-L. Machine Learning Spatial Geometry from Entanglement Features. arXiv:170901223 [cond-mat, physics:gr-qc, physics:hep-th, physics:quant-ph] . 2017 Sep 4; http://arxiv.org/abs/1709.01223
21. Han Z-Y, Wang J, Fan H, Wang L, Zhang P. Unsupervised Generative Modeling Using Matrix Product States. arXiv:170901662 [cond-mat, physics:quant-ph, stat] . 2017 Sep 5; http://arxiv.org/abs/1709.01662
22. Dunjko V, Briegel HJ. Machine learning \& artificial intelligence in the quantum domain. arXiv:170902779 [quant-ph] . 2017 Sep 8;http://arxiv.org/abs/1709.02779
23. Gan W-C, Shu F-W. Holography as deep learning. International Journal of Modern Physics D . 2017 Oct ;26(12):1743020. http://arxiv.org/abs/1705.05750
24. Robeva E, Seigal A. Duality of Graphical Models and Tensor Networks. arXiv:171001437 [quant-ph, stat] . 2017 Oct 3; http://arxiv.org/abs/1710.01437
25. Clark SR. Unifying Neural-network Quantum States and Correlator Product States via Tensor Networks. arXiv:171003545 [cond-mat, physics:quant-ph] . 2017 Oct 10; http://arxiv.org/abs/1710.03545
26. Kaubruegger R, Pastori L, Budich JC. Chiral Topological Phases from Artificial Neural Networks. arXiv:171004713 [cond-mat, physics:quant-ph] . 2017 Oct 12 ; http://arxiv.org/abs/1710.04713
27. Liu D, Ran S-J, Wittek P, Peng C, García RB, Su G, et al. Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures. arXiv:171004833 [cond-mat, physics:physics, physics:quant-ph, stat] . 2017 Oct 13;http://arxiv.org/abs/1710.04833
28. Zhang Y-H. Entanglement Entropy of Target Functions for Image Classification and Convolutional Neural Network. arXiv:171005520 [cond-mat] . 2017 Oct 16 ; http://arxiv.org/abs/1710.05520
29. Wang C, Zhai H. Unsupervised Learning of Frustrated Classical Spin Models I: Principle Component Analysis. Physical Review B . 2017 Oct 26 ;96(14).http://arxiv.org/abs/1706.07977
30. Pestun V, Vlassopoulos Y. Tensor network language model. arXiv:171010248 [cond-mat, stat] . 2017 Oct 27; http://arxiv.org/abs/1710.10248
31. Gao X, Zhang Z, Duan L. An efficient quantum algorithm for generative machine learning. arXiv:171102038 [quant-ph, stat] . 2017 Nov 6;http://arxiv.org/abs/1711.02038
32. Hallam A, Grant E, Stojevic V, Severini S, Green AG. Compact Neural Networks based on the Multiscale Entanglement Renormalization Ansatz. arXiv:171103357 [quant-ph] . 2017 Nov 9 ; http://arxiv.org/abs/1711.03357
33. Huang Y. Provably efficient neural network representation for image classification. arXiv:171104606 [cs] . 2017 Nov 13; http://arxiv.org/abs/1711.04606
34. Nomura Y, Darmawan AS, Yamaji Y, Imada M. Restricted-Boltzmann-Machine Learning for Solving Strongly Correlated Quantum Systems. Physical Review B . 2017 Nov 29 ;96(20). http://arxiv.org/abs/1709.06475
35. Cheng S, Chen J, Wang L. Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines. arXiv:171204144 [cond-mat, physics:physics, physics:quant-ph, stat] . 2017 Dec 12 ; http://arxiv.org/abs/1712.04144
36. Miyahara H, Sughiyama Y. A Quantum Extension of Variational Bayes Inference. arXiv:171204709 [cond-mat, physics:quant-ph, stat] . 2017 Dec 13 ;http://arxiv.org/abs/1712.04709
37. Verdon G, Broughton M, Biamonte J. A quantum algorithm to train neural networks using low-depth circuits. arXiv:171205304 [cond-mat, physics:quant-ph] . 2017 Dec 14 ; http://arxiv.org/abs/1712.05304
38. Stoudenmire EM. Learning Relevant Features of Data with Multi-scale Tensor Networks. arXiv:180100315 [cond-mat, stat] . 2017 Dec 31 ;http://arxiv.org/abs/1801.00315
