Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Gabriel Pescia"'
Autor:
Jane Kim, Gabriel Pescia, Bryce Fore, Jannes Nys, Giuseppe Carleo, Stefano Gandolfi, Morten Hjorth-Jensen, Alessandro Lovato
Publikováno v:
Communications Physics, Vol 7, Iss 1, Pp 1-12 (2024)
Abstract Ultra-cold Fermi gases exhibit a rich array of quantum mechanical properties, including the transition from a fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to a bosonic superfluid Bose-Einstein condensate (BEC). While these prop
Externí odkaz:
https://doaj.org/article/7c32d799950549a0a6bc27f18be95edc
Publikováno v:
Physical Review Research, Vol 4, Iss 2, p 023138 (2022)
We introduce a family of neural quantum states for the simulation of strongly interacting systems in the presence of spatial periodicity. Our variational state is parametrized in terms of a permutationally invariant part described by the Deep Sets ne
Externí odkaz:
https://doaj.org/article/0f749326c4a34db5bce2e8f95345851d
Autor:
Filippo Vicentini, Damian Hofmann, Attila Szabó, Dian Wu, Christopher Roth, Clemens Giuliani, Gabriel Pescia, Jannes Nys, Vladimir Vargas-Calderón, Nikita Astrakhantsev, Giuseppe Carleo
Publikováno v:
SciPost Physics Codebases
We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics. NetKet is built around neural-network quantum states and provides efficient algorithms for their evaluation and optimization. This new version is built on t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3776f0f3f3595146c6a2ea5cb172c475
https://doi.org/10.5167/uzh-220384
https://doi.org/10.5167/uzh-220384
Autor:
Filippo Vicentini, Damian Hofmann, Attila Szabó, Dian Wu, Christopher Roth, Clemens Giuliani, Gabriel Pescia, Jannes Nys, Vladimir Vargas-Calderón, Nikita Astrakhantsev, Giuseppe Carleo
Publikováno v:
SciPost Physics Codebases.
We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics. NetKet is built around neural quantum states and provides efficient algorithms for their evaluation and optimization. This new version is built on top of JA
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::41478eebb0f085e8c18f92b3999db214
https://doi.org/10.21468/scipostphyscodeb.7-r3.4
https://doi.org/10.21468/scipostphyscodeb.7-r3.4