Distinguishing an Anderson insulator from a many-body localized phase through space-time snapshots with neural networks

Artificial neural network for the single-particle localization problem in quasiperiodic one-dimensional lattices

“…The classification of extended and localized single-particle states through neural networks provides a useful benchmark to tackle the many-body localization problem using supervised learning techniques. Diagnosing manybody phases of matter requires, in addition to fully connected neural networks, the use of convolutional neural networks or principal component analysis to deal with the exponential dimension of quantum many-body states [25].…”

“…For instance, it has been used to predict crystal structures [17], solve impurity problems [18], and classify thermal and quantum phases of matter [19][20][21][22][23]. More recently, recurrent neural networks have been employed to build variational wave functions for quantum many-body problems [24], and convolutional neural networks have been used to distinguish the dynamics of an Anderson insulator from a many-body localized phase [25].…”

The structure of heavily doped impurity band in crystalline host

“…Machine learning methods have recently emerged as a valuable tool to study the quantum many-body physics problems [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Its ability to process high dimensional data and recognize complex patterns have been utilized to determine phase diagrams and phase transitions [23][24][25][26][27][28][29][30][31][32][33][34]. In particular, Convolutional Neural Network(CNN) [35] model, which initially is designed for image recognition, was widely used to study different kinds of phase transition problems including the Bose-Hubbard model [36], spin 1/2 Heisenberg model [37], quantum transverse-field Ising model [32] and etc.…”

Machine learning and high-performance computing in numerical simulation of quantum many-body systems

“…Machine learning methods have recently emerged as a valuable tool to study the quantum many-body physics problems [34,35,50,229,234,247,178,131,100,93,49,171,199,92,41,141,78,172]. Its ability to process high dimensional data and recognize complex patterns have been utilized to determine phase diagrams and phase transitions [237,165,223,142,29,192,126,61,115,267,266,121]. In particular, Convolutional Neural Network(CNN) [118] model, which initially is designed for image recognition, was widely used to study different kinds of phase transition problems including the Bose-Hubbard model [24], spin 1/2 Heisenberg model [224], quantum transverse-field Ising model [267] and etc.…”