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- W3152015274 abstract "The one-body reduced density matrix $gamma$ 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 $mathcal{F}[gamma]$ that recovers quantum correlations exactly. Based on a novel decomposition of $gamma$, 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 an Euclidian space. This simplification of the search approach allows us to use standard machine-learning methods to perform a quite efficient computation of $mathcal{F}[gamma]$. For the Bose-Hubbard model, we present a comparison between our approach and Quantum Monte Carlo." @default.
- W3152015274 created "2021-04-13" @default.
- W3152015274 creator A5021995308 @default.
- W3152015274 creator A5051281561 @default.
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- W3152015274 date "2021-04-07" @default.
- W3152015274 modified "2023-09-27" @default.
- W3152015274 title "Machine Learning Universal Bosonic Functionals" @default.
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- W3152015274 hasPublicationYear "2021" @default.
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