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Machine learning out-of-equilibrium phases of matter
Jordan Venderley, Vedika Khemani, Eun-Ah Kim
Neural network based machine learning is emerging as a powerful tool for obtaining phase diagrams when traditional regression schemes using local equilibrium order parameters are n…
Operator spreading and the emergence of dissipative hydrodynamics under unitary evolution with conservation laws
Vedika Khemani, Ashvin Vishwanath, D. A. Huse
We study the scrambling of local quantum information in chaotic many-body systems in the presence of a locally conserved quantity like charge or energy that moves diffusively. The…
Obtaining highly excited eigenstates of the localized XX chain via DMRG-X
Trithep Devakul, Vedika Khemani, Frank Pollmann +2
We benchmark a variant of the recently introduced DMRG-X algorithm against exact results for the localized random field XX chain. We find that the eigenstates obtained via DMRG-X e…
Two universality classes for the many-body localization transition
Vedika Khemani, D. N. Sheng, David A. Huse
We provide a systematic comparison of the many-body localization transition in spin chains with nonrandom quasiperiodic vs. random fields. We find evidence that these belong to two…