| Autor: |
Jonathan Schmidt, Matteo Fadel, Carlos L. Benavides-Riveros |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Physical Review Research, Vol 3, Iss 3, p L032063 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2643-1564 |
| DOI: |
10.1103/PhysRevResearch.3.L032063 |
| Popis: |
The one-body reduced density matrix γ plays a fundamental role in describing and predicting quantum features of bosonic systems, such as Bose-Einstein condensation. The recently proposed reduced density matrix functional theory for bosonic ground states establishes the existence of a universal functional F[γ] that recovers quantum correlations exactly. Based on a decomposition of γ, we have developed a method to design reliable approximations for such universal functionals: Our results suggest that for translational invariant systems the constrained search approach of functional theories can be transformed into an unconstrained problem through a parametrization of a Euclidian space. This simplification of the search approach allows us to use standard machine learning methods to perform a quite efficient computation of both F[γ] and its functional derivative. For the Bose-Hubbard model, we present a comparison between our approach and the quantum Monte Carlo method. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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