Machine learning on raw spectral data can separate the many-body critical phase from ergodic and localized phases in a quasiperiodic chain and reproduce known critical exponents from scaling collapse.
These computations can result in both positive and negative values propagating through the network
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Supervised and unsupervised learning of the many-body critical phase, phase transitions, and critical exponents in disordered quantum systems
Machine learning on raw spectral data can separate the many-body critical phase from ergodic and localized phases in a quasiperiodic chain and reproduce known critical exponents from scaling collapse.