Importance of realistic phase space representations of initial quantum fluctuations using the stochastic mean-field approach for fermions
Bulent Yilmaz, Denis Lacroix, Resul Curebal

TL;DR
This paper demonstrates that using a realistic phase-space representation, such as the Husimi distribution, within the stochastic mean-field approach significantly improves the modeling of initially correlated fermionic quantum systems, especially beyond Gaussian assumptions.
Contribution
The paper introduces a method combining the stochastic mean-field approach with Husimi distribution to better represent initial quantum fluctuations in correlated fermionic systems.
Findings
Gaussian phase-space assumption fails for correlated states
Husimi distribution improves initial state representation
Method achieves perfect agreement in weak coupling regime
Abstract
In the stochastic mean-field (SMF) approach, an ensemble of initial values for a selected set of one-body observables is formed by stochastic sampling from a phase-space distribution that reproduces the initial quantum fluctuations. Independent mean-field evolutions are performed with each set of initial values followed by averaging over the resulting ensemble. This approach has been recently shown to be rather versatile and accurate in describing the correlated dynamics beyond the independent particle picture. In the original formulation of SMF, it was proposed to use a Gaussian assumption for the phase-space distribution. This assumption turns out to be rather effective when the dynamics of an initially uncorrelated state is considered, which was the case in all applications of this approach up to now. Using the Lipkin-Meshkov-Glick (LMG) model, we show that such an assumption might…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
