all AI news
Ab-initio study of interacting fermions at finite temperature with neural canonical transformation. (arXiv:2105.08644v2 [cond-mat.str-el] UPDATED)
April 4, 2022, 1:11 a.m. | Hao Xie, Linfeng Zhang, Lei Wang
cs.LG updates on arXiv.org arxiv.org
We present a variational density matrix approach to the thermal properties of
interacting fermions in the continuum. The variational density matrix is
parametrized by a permutation equivariant many-body unitary transformation
together with a discrete probabilistic model. The unitary transformation is
implemented as a quantum counterpart of neural canonical transformation, which
incorporates correlation effects via a flow of fermion coordinates. As the
first application, we study electrons in a two-dimensional quantum dot with an
interaction-induced crossover from Fermi liquid to Wigner …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Lead Data Modeler
@ Sherwin-Williams | Cleveland, OH, United States