Superpositions of tree-tensor networks for single-reference ground states in the strong correlation regime

TL;DR

This paper demonstrates that superpositions of tree-tensor networks (STTN) can efficiently and accurately represent strongly correlated ground states in small Hubbard clusters, outperforming traditional methods in parameter efficiency.

Contribution

It introduces the STTN tensor decomposition method for coupled-cluster calculations, enabling compact and accurate modeling of strongly correlated ground states without truncation.

Findings

STTN provides more accurate ground state representations as system size increases.

STTN uses fewer parameters than CCSD, showing high efficiency.

Combining multiple channels in tensor decompositions improves accuracy.

Abstract

The fermionic many-body problem in the strong correlation regime is notoriously difficult to tackle. In a previous work (Phys. Rev. B 101, 045109 (2020)), we have proposed to extend the single-reference coupled-cluster (SRCC) method to the strong correlation regime using low-rank tensor decompositions (LRTD) to express the cluster operator, without truncating it with respect to the number of excitations. For that purpose, we have proposed a new type of LRTD called ``superpositions of tree-tensor networks'' (STTN), which use the same set of building blocs to define all the tensors involved in the CC equations, and combine different ``channels'', i.e. different types of pairing among excited particles and holes, in the decomposition of a given tensor. Those two principles are aimed at globally minimizing the total number of free parameters required to accurately represent the ground state. In this work, we show that STTN can indeed be compact and accurate representations of strongly correlated ground states by using them to express the CC cluster operator amplitudes and wave function coefficients of exact grounds states of small two-dimensional Hubbard clusters, at half-filling, up to three particle-hole excitations. We show the compactness of STTN by using a number of free parameters smaller than the number of equations in the CCSD approximation, i.e. much smaller than the number of fitted tensor elements. We find that, for the systems considered, the STTN are more accurate as the size of the system increases and that combining different channels in the decompositions of the most strongly correlated tensors is crucial to obtain good accuracy.