Stochastic tensor contraction for quantum chemistry

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Authors: Jiace Sun, Garnet Kin-Lic Chan
Cited by: 1
References: 58

Abstract

Many computational methods in ab initio quantum chemistry are formulated in terms of high-order tensor contractions, whose cost determines the size of system that can be studied. We introduce stochastic tensor contraction to perform such operations with greatly reduced cost, and present its application to the gold-standard quantum chemistry method, coupled cluster theory with up to perturbative triples. For total energy errors more stringent than chemical accuracy, we reduce the computational scaling to that of exact mean-field theory, while starting to approach the practical mean-field absolute cost, thereby challenging the existing cost-to-accuracy landscape. Benchmarks against state-of-the-art local correlation approximations further show that we achieve an order-of-magnitude improvement in both total computation time and error, with significantly reduced sensitivity to system dimensionality and electron delocalization. We conclude that stochastic tensor contraction is a powerful computational primitive to accelerate a wide range of quantum chemistry.

Fields of Study & Subjects

Quantum many-body systems Machine Learning in Materials Science Quantum Computing Algorithms and Architecture

Publication details

Journal / source
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Volume
123
Issue
33
Pages
e2609149123–e2609149123
Publication date
13 August 2026
Language
EN