Clifford augmented density matrix renormalization group for <i>ab initio</i> quantum chemistry

The recently proposed Clifford augmented density matrix renormalization group (CA-DMRG) method seamlessly integrates Clifford circuits with matrix product states and takes advantage of the expression power from both. CA-DMRG has been shown to be able to achieve higher accuracy than standard DMRG on commonly used lattice models, with only moderate computational overhead compared to the latter. In this work, we propose an efficient scheme in CA-DMRG to deal with ab initio quantum chemistry Hamiltonians and apply it to study several molecular systems. Our numerical results show that CA-DMRG can reach higher accuracy than DMRG using the same bond dimension, pointing out a promising route to push the boundary of solving ab initio quantum chemistry with strong static correlations.

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Publication Details

Journal
Physical review. B./Physical review. B
Published
2025-11-12
DOI
https://doi.org/10.1103/4ng4-vzz6
Citations
2
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
1.04
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article

Clifford augmented density matrix renormalization group for ab initio quantum chemistry

Honghui Shang, Chu Guo, Jinlong Yang, Lizhong Fu
2 citations
Physical review. B./Physical review. B
Machine Learning in Materials Science
1.04
article

Clifford augmented density matrix renormalization group for ab initio quantum chemistry

Honghui Shang, Chu Guo, Jinlong Yang, Lizhong Fu
article en
2 citations

Abstract

The recently proposed Clifford augmented density matrix renormalization group (CA-DMRG) method seamlessly integrates Clifford circuits with matrix product states and takes advantage of the expression power from both. CA-DMRG has been shown to be able to achieve higher accuracy than standard DMRG on commonly used lattice models, with only moderate computational overhead compared to the latter. In this work, we propose an efficient scheme in CA-DMRG to deal with ab initio quantum chemistry Hamiltonians and apply it to study several molecular systems. Our numerical results show that CA-DMRG can reach higher accuracy than DMRG using the same bond dimension, pointing out a promising route to push the boundary of solving ab initio quantum chemistry with strong static correlations.

Physical review. B./Physical review. BVol. 112(19)
University of Science and Technology of China (CN), National University of Defense Technology (CN)
Openalex Percentile: Top 21%
Machine Learning in Materials Science
1.04
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Clifford augmented density matrix renormalization group for <i>ab initio</i> quantum chemistry — Honghui Shang, Chu Guo, et al. · Physical review. B./Physical review. B (2025) | TGRS Research Map | TGRS