Built on

Raghavendra Chalapathy and Sanjay Chawla, “Deep learning for anomaly detection: A survey,” arXiv preprint arXiv:1901.03407 (2019), arXiv:1901.03407
Original
1901
Earlier work this paper cites.
Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, and Lenka Zdeborová, “Machine learning and the physical sciences,” (2019), 10.1103/RevModPhys.91.045002 , arXiv:1903.10563
Original
1903
Earlier work this paper cites.
Kazuya Shinjo, Kakeru Sasaki, Satoru Hase, Shigetoshi Sota, Satoshi Ejima, Seiji Yunoki, and Takami Tohyama, “Machine Learning Phase Diagram in the Half-filled One-dimensional Extended Hubbard Model,” (2019), 10.7566/JPSJ.88.065001 , arXiv:1904.06032
Original
1904
Earlier work this paper cites.
Hugo Théveniaut and Fabien Alet, “Neural network setups for a precise detection of the many-body localization transition: finite-size scaling and limitations,” Physical Review B 100
Original
1904
Earlier work this paper cites.
Yuan-Hong Tsai, Meng-Jer Yu, Yu-Hao Hsu, and Ming-Chiang Chung, “Deep learning of topological phase transitions from entanglement aspects,” (2019) , arXiv:1909.04784
Original
1909
Earlier work this paper cites.
AF Andreev and IM Lifshits, “Quantum theory of defects in crystals,” Zhur Eksper Teoret Fiziki 56
1969
Earlier work this paper cites.
G. V. Chester, “Speculations on bose-einstein condensation and quantum crystals,” Phys. Rev. A 2
1970
Earlier work this paper cites.
Steven R. White, “Density matrix formulation for quantum renormalization groups,” Physical Review Letters 69
1992
Earlier work this paper cites.
Steven R. White, “Density-matrix algorithms for quantum renormalization groups,” Physical Review B 48
1993
Earlier work this paper cites.
Till D. Kuehner and H. Monien, “Phases of the one-dimensional Bose-Hubbard model,” (1997), 10.1103/PhysRevB.58.R14741 , arXiv:9712307 [cond-mat]
1997
Earlier work this paper cites.
Till D. Kuehner, Steven R. White, and H. Monien, “The one-dimensional Bose-Hubbard Model with nearest-neighbor interaction,” (1999), 10.1103/PhysRevB.61.12474 , arXiv:9906019 [cond-mat]
1999
Earlier work this paper cites.
Guifre Vidal, “Efficient classical simulation of slightly entangled quantum computations,” (2003), 10.1103/PhysRevLett.91.147902 , arXiv:0301063 [quant-ph]
2003
Earlier work this paper cites.
G. Vidal, “Classical simulation of infinite-size quantum lattice systems in one spatial dimension,” (2006), 10.1103/PhysRevLett.98.070201 , arXiv:0605597 [cond-mat]
2006
Earlier work this paper cites.
Laura Urba, Emil Lundh, and Anders Rosengren, “One-dimensional extended Bose-Hubbard model with a confining potential: A DMRG analysis,” Journal of Physics B: Atomic, Molecular and Optical Physics 39
2006
Earlier work this paper cites.
G. G. Batrouni, F. Hebert, and R. T. Scalettar, “Supersolid phases in the one dimensional extended soft core Bosonic Hubbard model,” (2006), 10.1103/PhysRevLett.97.087209 , arXiv:0605218 [cond-mat]
2006
Earlier work this paper cites.
P. Zanardi, M. Cozzini, and P. Giorda, “Ground state fidelity and quantum phase transitions in free Fermi systems,” (2006), 10.1088/1742-5468/2007/02/L02002 , arXiv:0606130 [quant-ph]
2007
Earlier work this paper cites.
Ian P. McCulloch, “From density-matrix renormalization group to matrix product states,” (2007), 10.1088/1742-5468/2007/10/P10014 , arXiv:0701428 [cond-mat]
2007
Earlier work this paper cites.
R. Orús and G. Vidal, “Infinite time-evolving block decimation algorithm beyond unitary evolution,” Physical Review B 78
2008
Earlier work this paper cites.
Erez Berg, Emanuele G. Dalla Torre, Thierry Giamarchi, and Ehud Altman, “Rise and fall of hidden string order of lattice bosons,” (2008), 10.1103/PhysRevB.77.245119 , arXiv:0803.2851
Original
2008
Earlier work this paper cites.

Similar

Tapan Mishra, Ramesh V. Pai, S. Ramanan, Meetu Sethi Luthra, and B. P. Das, “Supersolid and solitonic phases in one-dimensional Extended Bose-Hubbard model,” (2009), 10.1103/PhysRevA.80.043614 , arXiv:0907.1258
Original
2009
Cited alongside, same era.
Ulrich Schollwöck, “The density-matrix renormalization group in the age of matrix product states,” Annals of Physics 326
2011
Cited alongside, same era.
Xiaolong Deng and Luis Santos, “Entanglement spectrum of one-dimensional extended Bose-Hubbard models,” (2011), 10.1103/PhysRevB.84.085138 , arXiv:1104.5157
Original
2011
Cited alongside, same era.
M. A. Cazalilla, R. Citro, T. Giamarchi, E. Orignac, and M. Rigol, “One dimensional Bosons: From Condensed Matter Systems to Ultracold Gases,” (2011), 10.1103/RevModPhys.83.1405 , arXiv:1101.5337
Original
2011
Cited alongside, same era.
Davide Rossini and Rosario Fazio, “Phase diagram of the extended Bose Hubbard model,” (2012), 10.1088/1367-2630/14/6/065012 , arXiv:1204.5964

Then

Pengfei Zhang, Huitao Shen, and Hui Zhai, “Machine learning topological invariants with neural networks,” (2017), arXiv:1708.09401
Original
2017
Later among the works it cites.
Peter Broecker, Juan Carrasquilla, Roger G. Melko, and Simon Trebst, “Machine learning quantum phases of matter beyond the fermion sign problem,” Scientific Reports 8
Original
2017
Later among the works it cites.
Keima Kawaki, Yoshihito Kuno, and Ikuo Ichinose, “Phase diagrams of the extended bose-hubbard model in one dimension by monte-carlo simulation with the help of a stochastic-series expansion,” Phys. Rev. B 95
Original
2017
Later among the works it cites.
Donghwoon Kwon, Hyunjoo Kim, Jinoh Kim, Sang C Suh, Ikkyun Kim, and Kuinam J Kim, “A survey of deep learning-based network anomaly detection,” Cluster Computing , 1–13 (2017)
2017
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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Reading the bibliography…

2020

Unsupervised phase discovery with deep anomaly detection

Kottmann, Korbinian, Huembeli, Patrick, Lewenstein, Maciej et al.

Understand

We demonstrate how to explore phase diagrams with automated and unsupervised machine learning to find regions of interest for possible new phases.

In contrast to supervised learning, where data is classified using predetermined labels, we here perform anomaly detection, where the task is to differentiate a normal data set, composed of one or several classes, from anomalous data.
Asa paradigmatic example, we explore the phase diagram of the extended Bose Hubbard model in one dimension at exact integer filling and employ deep neural networks to determine the entire phase diagram in a completely unsupervised and automated fashion.
As input data for learning, we first use the entanglement spectra and central tensors derived from tensor-networks algorithms for ground-state computation and later we extend our method and use experimentally accessible data such as low-order correlation functions as inputs.

Built on

Raghavendra Chalapathy and Sanjay Chawla, “Deep learning for anomaly detection: A survey,” arXiv preprint arXiv:1901.03407 (2019), arXiv:1901.03407
Original
1901
Earlier work this paper cites.
Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, and Lenka Zdeborová, “Machine learning and the physical sciences,” (2019), 10.1103/RevModPhys.91.045002 , arXiv:1903.10563
Original
1903
Earlier work this paper cites.
Kazuya Shinjo, Kakeru Sasaki, Satoru Hase, Shigetoshi Sota, Satoshi Ejima, Seiji Yunoki, and Takami Tohyama, “Machine Learning Phase Diagram in the Half-filled One-dimensional Extended Hubbard Model,” (2019), 10.7566/JPSJ.88.065001 , arXiv:1904.06032
Original
1904
Earlier work this paper cites.
Hugo Théveniaut and Fabien Alet, “Neural network setups for a precise detection of the many-body localization transition: finite-size scaling and limitations,” Physical Review B 100
Original
1904
Earlier work this paper cites.
Yuan-Hong Tsai, Meng-Jer Yu, Yu-Hao Hsu, and Ming-Chiang Chung, “Deep learning of topological phase transitions from entanglement aspects,” (2019) , arXiv:1909.04784
Original
1909
Earlier work this paper cites.
AF Andreev and IM Lifshits, “Quantum theory of defects in crystals,” Zhur Eksper Teoret Fiziki 56
1969
Earlier work this paper cites.
G. V. Chester, “Speculations on bose-einstein condensation and quantum crystals,” Phys. Rev. A 2
1970
Earlier work this paper cites.
Steven R. White, “Density matrix formulation for quantum renormalization groups,” Physical Review Letters 69
1992
Earlier work this paper cites.
Steven R. White, “Density-matrix algorithms for quantum renormalization groups,” Physical Review B 48
1993
Earlier work this paper cites.
Till D. Kuehner and H. Monien, “Phases of the one-dimensional Bose-Hubbard model,” (1997), 10.1103/PhysRevB.58.R14741 , arXiv:9712307 [cond-mat]
1997
Earlier work this paper cites.
Till D. Kuehner, Steven R. White, and H. Monien, “The one-dimensional Bose-Hubbard Model with nearest-neighbor interaction,” (1999), 10.1103/PhysRevB.61.12474 , arXiv:9906019 [cond-mat]
1999
Earlier work this paper cites.
Guifre Vidal, “Efficient classical simulation of slightly entangled quantum computations,” (2003), 10.1103/PhysRevLett.91.147902 , arXiv:0301063 [quant-ph]
2003
Earlier work this paper cites.
G. Vidal, “Classical simulation of infinite-size quantum lattice systems in one spatial dimension,” (2006), 10.1103/PhysRevLett.98.070201 , arXiv:0605597 [cond-mat]
2006
Earlier work this paper cites.
Laura Urba, Emil Lundh, and Anders Rosengren, “One-dimensional extended Bose-Hubbard model with a confining potential: A DMRG analysis,” Journal of Physics B: Atomic, Molecular and Optical Physics 39
2006
Earlier work this paper cites.
G. G. Batrouni, F. Hebert, and R. T. Scalettar, “Supersolid phases in the one dimensional extended soft core Bosonic Hubbard model,” (2006), 10.1103/PhysRevLett.97.087209 , arXiv:0605218 [cond-mat]
2006
Earlier work this paper cites.
P. Zanardi, M. Cozzini, and P. Giorda, “Ground state fidelity and quantum phase transitions in free Fermi systems,” (2006), 10.1088/1742-5468/2007/02/L02002 , arXiv:0606130 [quant-ph]
2007
Earlier work this paper cites.
Ian P. McCulloch, “From density-matrix renormalization group to matrix product states,” (2007), 10.1088/1742-5468/2007/10/P10014 , arXiv:0701428 [cond-mat]
2007
Earlier work this paper cites.
R. Orús and G. Vidal, “Infinite time-evolving block decimation algorithm beyond unitary evolution,” Physical Review B 78
2008
Earlier work this paper cites.
Erez Berg, Emanuele G. Dalla Torre, Thierry Giamarchi, and Ehud Altman, “Rise and fall of hidden string order of lattice bosons,” (2008), 10.1103/PhysRevB.77.245119 , arXiv:0803.2851
Original
2008
Earlier work this paper cites.

Similar

Tapan Mishra, Ramesh V. Pai, S. Ramanan, Meetu Sethi Luthra, and B. P. Das, “Supersolid and solitonic phases in one-dimensional Extended Bose-Hubbard model,” (2009), 10.1103/PhysRevA.80.043614 , arXiv:0907.1258
Original
2009
Cited alongside, same era.
Ulrich Schollwöck, “The density-matrix renormalization group in the age of matrix product states,” Annals of Physics 326
2011
Cited alongside, same era.
Xiaolong Deng and Luis Santos, “Entanglement spectrum of one-dimensional extended Bose-Hubbard models,” (2011), 10.1103/PhysRevB.84.085138 , arXiv:1104.5157
Original
2011
Cited alongside, same era.
M. A. Cazalilla, R. Citro, T. Giamarchi, E. Orignac, and M. Rigol, “One dimensional Bosons: From Condensed Matter Systems to Ultracold Gases,” (2011), 10.1103/RevModPhys.83.1405 , arXiv:1101.5337
Original
2011
Cited alongside, same era.
Davide Rossini and Rosario Fazio, “Phase diagram of the extended Bose Hubbard model,” (2012), 10.1088/1367-2630/14/6/065012 , arXiv:1204.5964

Then

Pengfei Zhang, Huitao Shen, and Hui Zhai, “Machine learning topological invariants with neural networks,” (2017), arXiv:1708.09401
Original
2017
Later among the works it cites.
Peter Broecker, Juan Carrasquilla, Roger G. Melko, and Simon Trebst, “Machine learning quantum phases of matter beyond the fermion sign problem,” Scientific Reports 8
Original
2017
Later among the works it cites.
Keima Kawaki, Yoshihito Kuno, and Ikuo Ichinose, “Phase diagrams of the extended bose-hubbard model in one dimension by monte-carlo simulation with the help of a stochastic-series expansion,” Phys. Rev. B 95
Original
2017
Later among the works it cites.
Donghwoon Kwon, Hyunjoo Kim, Jinoh Kim, Sang C Suh, Ikkyun Kim, and Kuinam J Kim, “A survey of deep learning-based network anomaly detection,” Cluster Computing , 1–13 (2017)
2017
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2012

Cited alongside, same era.

Roman Orus, “A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States,” (2013), 10.1016/j.aop.2014.06.013 , arXiv:1306.2164

Original

2014

Cited alongside, same era.

Satoshi Ejima, Florian Lange, and Holger Fehske, “Spectral and Entanglement Properties of the Bosonic Haldane Insulator,” (2014), 10.1103/PhysRevLett.113.020401 , arXiv:1407.2852

Original

2014

Cited alongside, same era.

Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” (2015), software available from tensorflow.org

2015

Cited alongside, same era.

Lei Wang, “Discovering phase transitions with unsupervised learning,” Physical Review B 94

Original

2016

Cited alongside, same era.

Dong-Ling Deng, Xiaopeng Li, and S. Das Sarma, “Exact machine learning topological states,” arXiv:1609.09060 (2016)

Original

2016

Cited alongside, same era.

Zohar Nussinov, Peter Ronhovde, Dandan Hu, S Chakrabarty, Bo Sun, Nicholas A Mauro, and Kisor K Sahu, “Inference of hidden structures in complex physical systems by multi-scale clustering,” in Information Science for Materials Discovery and Design (Springer, 2016) pp. 115–138

2016

Cited alongside, same era.

Philipp Seeböck, Sebastian Waldstein, Sophie Klimscha, Bianca S Gerendas, René Donner, Thomas Schlegl, Ursula Schmidt-Erfurth, and Georg Langs, “Identifying and categorizing anomalies in retinal imaging data,” (2016), arXiv:1612.00686

Original

2016

Cited alongside, same era.

Kenji Kawaguchi, Leslie Pack Kaelbling, and Yoshua Bengio, “Generalization in deep learning,” (2017), arXiv:1710.05468

Original

2017