Efficient and scalable wave function compression using corner hierarchical matrices.

J Chem Phys

Institute for Advanced Computational Science, Stony Brook University, Stony Brook, New York 11794, USA.

Published: November 2024

AI Article Synopsis

  • Current methods like complete active space and full configuration interaction limit quantum chemists' ability to analyze strongly correlated electronic structures due to exponential scaling issues.
  • The introduction of corner hierarchically approximated CI (CHACI) improves wave function compression using a new type of hierarchical matrix that benefits from low-rank decomposition techniques.
  • Tests on the molecule dodecacene show that CHACI achieves better compression rates than traditional methods, with advantages from specific strategies like focusing on the upper-left corner of the CI vector, sorting the vector before compression, and optimizing block ranks for higher information density.

Article Abstract

The exponential scaling of complete active space and full configuration interaction (CI) calculations limits the ability of quantum chemists to simulate the electronic structures of strongly correlated systems. Herein, we present corner hierarchically approximated CI (CHACI), an approach to wave function compression based on corner hierarchical matrices (CH-matrices)-a new variant of hierarchical matrices based on block-wise low-rank decomposition. By application to dodecacene, a strongly correlated molecule, we demonstrate that CH matrix compression provides superior compression compared to truncated global singular value decomposition. The compression ratio is shown to improve with increasing active space size. By comparison of several alternative schemes, we demonstrate that superior compression is achieved by (a) using a blocking approach that emphasizes the upper-left corner of the CI vector, (b) sorting the CI vector prior to compression, and (c) optimizing the rank of each block to maximize information density.

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Source
http://dx.doi.org/10.1063/5.0231409DOI Listing

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