Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Basile Herzog"'
Autor:
Basile Herzog, Alejandro Gallo, Felix Hummel, Michael Badawi, Tomáš Bučko, Sébastien Lebègue, Andreas Grüneis, Dario Rocca
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-7 (2024)
Abstract Density functional theory is the workhorse of materials simulations. Unfortunately, the quality of results often varies depending on the specific choice of the exchange-correlation functional, which significantly limits the predictive power
Externí odkaz:
https://doaj.org/article/ccc17babc74e485d9caca9d2e92ddaf0
Publikováno v:
Scientific Reports, Vol 10, Iss 1, Pp 1-8 (2020)
Abstract We propose a new quantum numerical scheme to control the dynamics of a quantum walker in a two dimensional space–time grid. More specifically, we show how, introducing a quantum memory for each of the spatial grid, this result can be achie
Externí odkaz:
https://doaj.org/article/5b2e6bf212c24ceeba2246030abf28c9
Autor:
Giuseppe Di Molfetta, Basile Herzog
Publikováno v:
Algorithms, Vol 13, Iss 11, p 305 (2020)
We provide numerical evidence that the nonlinear searching algorithm introduced by Wong and Meyer, rephrased in terms of quantum walks with effective nonlinear phase, can be extended to the finite 2-dimensional grid, keeping the same computational ad
Externí odkaz:
https://doaj.org/article/6d77fa5114fe41109528cffaf06a46e6
Autor:
Basile Herzog, Alejandro Gallo, Felix Hummel, Michael Badawi, Tomáš Bučko, Sébastien Lebègue, Andreas Grüneis, Dario Rocca
Density functional theory is the workhorse of materials simulations. Unfortunately, the quality of results often varies depending on the specific choice of the exchange-correlation functional, and this significantly limits the predictive power of thi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::34096b6a0758b26c99b10edf3e71a70a
https://doi.org/10.26434/chemrxiv-2023-mvsxn
https://doi.org/10.26434/chemrxiv-2023-mvsxn
Autor:
Basile Herzog, Maurício Chagas da Silva, Bastien Casier, Michael Badawi, Fabien Pascale, Tomáš Bučko, Sébastien Lebègue, Dario Rocca
Machine learning thermodynamic perturbation theory (MLPT) is a promising approach to compute finite temperature properties when the goal is to compare several different levels of ab initio theory and/or to apply highly expensive computational methods
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c06d42849f69c114857ed3d07877b70
Publikováno v:
Scientific Reports
Scientific Reports, 2020, 10 (1), pp.21354. ⟨10.1038/s41598-020-78455-3⟩
Scientific Reports, Nature Publishing Group, 2020, 10 (1), pp.21354. ⟨10.1038/s41598-020-78455-3⟩
Scientific Reports, Vol 10, Iss 1, Pp 1-8 (2020)
Scientific Reports, 2020, 10 (1), pp.21354. ⟨10.1038/s41598-020-78455-3⟩
Scientific Reports, Nature Publishing Group, 2020, 10 (1), pp.21354. ⟨10.1038/s41598-020-78455-3⟩
Scientific Reports, Vol 10, Iss 1, Pp 1-8 (2020)
We propose a new quantum numerical scheme to control the dynamics of a quantum walker in a two dimensional space-time grid. More specifically, we show how, introducing a quantum memory for each of the spatial grid, this result can be achieved simply
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e350b5fc2a740fb397c7ee017c5cf2f3
http://arxiv.org/abs/2009.10408
http://arxiv.org/abs/2009.10408