Beyond a Richardson-Gaudin mean-field: Slater-Condon rules and perturbation theory
Paul A. Johnson

TL;DR
This paper extends Richardson-Gaudin mean-field theory by deriving efficient transition density matrix expressions, establishing Slater-Condon-like rules, and developing a perturbation method that rivals configuration interaction in accuracy for strongly correlated electrons.
Contribution
It introduces optimal transition density matrix formulas, identifies Slater-Condon analogues based on singular value analysis, and proposes a perturbative approach comparable to configuration interaction.
Findings
Transition density matrices can be computed efficiently with costs similar to reduced density matrices.
Slater-Condon rules are extended to Richardson-Gaudin states based on singular value analysis.
A perturbation method achieves accuracy close to configuration interaction, with feasible computational cost.
Abstract
Richardson-Gaudin states provide a basis of the Hilbert space for strongly correlated electrons. In this study, optimal expressions for the transition density matrix elements between Richardson-Gaudin states are obtained with a cost comparable with the corresponding reduced density matrix elements. Analogues of the Slater-Condon rules are identified based on the number of near-zero singular values of the RG state overlap matrix. Finally, a perturbative approach is shown to be close in quality to a configuration interaction of Richardson-Gaudin states while being feasible to compute.
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.
Taxonomy
TopicsNeural Networks and Reservoir Computing
