Unsupervised learning eigenstate phases of matter
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[1] Arthur Zimek,et al. Density-Based Clustering Validation , 2014, SDM.
[2] J. Bardarson,et al. Many-body localization in a disordered quantum Ising chain. , 2014, Physical review letters.
[3] M. Schreiber,et al. Observation of many-body localization of interacting fermions in a quasirandom optical lattice , 2015, Science.
[4] Ye-Hua Liu,et al. Discriminative Cooperative Networks for Detecting Phase Transitions. , 2017, Physical review letters.
[5] Wenjian Hu,et al. Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination. , 2017, Physical review. E.
[6] B. A. Lindquist,et al. Unsupervised machine learning for detection of phase transitions in off-lattice systems. I. Foundations. , 2018, The Journal of chemical physics.
[7] Roger G. Melko,et al. Machine learning phases of matter , 2016, Nature Physics.
[8] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[9] Thomas G. Dietterich,et al. In Advances in Neural Information Processing Systems 12 , 1991, NIPS 1991.
[10] Yi Zhang,et al. Quantum Loop Topography for Machine Learning. , 2016, Physical review letters.
[11] T. Ohtsuki,et al. Deep Learning the Quantum Phase Transitions in Random Electron Systems: Applications to Three Dimensions , 2016, 1612.04909.
[12] D. Huse,et al. Many-body localization phase transition , 2010, 1003.2613.
[13] C Casert,et al. Interpretable machine learning for inferring the phase boundaries in a nonequilibrium system. , 2018, Physical review. E.
[14] David A. Huse,et al. Localization-protected quantum order , 2013, 1304.1158.
[15] Joaquin F. Rodriguez-Nieva,et al. Identifying topological order through unsupervised machine learning , 2018, Nature Physics.
[16] Juan Carrasquilla,et al. Machine learning quantum phases of matter beyond the fermion sign problem , 2016, Scientific Reports.
[17] Sebastian Johann Wetzel,et al. Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders , 2017, Physical review. E.
[18] Matthias Troyer,et al. Solving the quantum many-body problem with artificial neural networks , 2016, Science.
[19] S. Huber,et al. Learning phase transitions by confusion , 2016, Nature Physics.
[20] Lei Wang,et al. Discovering phase transitions with unsupervised learning , 2016, 1606.00318.
[21] Titus Neupert,et al. Probing many-body localization with neural networks , 2017, 1704.01578.
[22] Leland McInnes,et al. hdbscan: Hierarchical density based clustering , 2017, J. Open Source Softw..
[23] Y. Hatsugai,et al. Phase diagram of a disordered higher-order topological insulator: A machine learning study , 2018, Physical Review B.
[24] Yi Zhang,et al. Machine learning Z 2 quantum spin liquids with quasiparticle statistics , 2017, 1705.01947.
[25] Fisher,et al. Critical behavior of random transverse-field Ising spin chains. , 1995, Physical review. B, Condensed matter.
[26] Vedika Khemani,et al. Machine Learning Out-of-Equilibrium Phases of Matter. , 2017, Physical review letters.
[27] Dominik Endres,et al. A new metric for probability distributions , 2003, IEEE Transactions on Information Theory.
[28] Gil Refael,et al. Hilbert-Glass Transition: New Universality of Temperature-Tuned Many-Body Dynamical Quantum Criticality , 2013, 1307.3253.
[29] Manuel Scherzer,et al. Machine Learning of Explicit Order Parameters: From the Ising Model to SU(2) Lattice Gauge Theory , 2017, 1705.05582.
[30] Mattias Jönsson,et al. Detecting the Many-Body Localization Transition with Machine Learning Techniques , 2018 .
[31] Peter Wittek,et al. Adversarial Domain Adaptation for Identifying Phase Transitions , 2017, ArXiv.
[32] Nicolas Regnault,et al. Many-body localization and thermalization: Insights from the entanglement spectrum , 2016, 1603.00880.
[33] B. Bauer,et al. Area laws in a many-body localized state and its implications for topological order , 2013, 1306.5753.
[34] 小谷 正雄. 日本物理学会誌及びJournal of the Physical Society of Japanの月刊について , 1955 .