preprint · arXiv (Cornell University) · 2026

Stochastic tensor contraction for quantum chemistry

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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 mean-field theory, while starting to approach the 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.

Cite this paper

APA 7

Sun, J., & Chan, G. K.-L. (2026). Stochastic tensor contraction for quantum chemistry. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2602.17158

MLA 9

Sun, Jiace, and Garnet Kin-Lic Chan. “Stochastic tensor contraction for quantum chemistry.” arXiv (Cornell University), 2026, https://doi.org/10.48550/arxiv.2602.17158.

Chicago

Sun, Jiace, and Garnet Kin-Lic Chan. 2026. “Stochastic tensor contraction for quantum chemistry.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.2602.17158.

Harvard

Sun, J. and Chan, G.K.-L. (2026) ‘Stochastic tensor contraction for quantum chemistry’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.2602.17158.

BibTeX

@misc{sun2026stochastic,
  title = {{Stochastic tensor contraction for quantum chemistry}},
  author = {Sun, Jiace and Chan, Garnet Kin-Lic},
  journal = {arXiv (Cornell University)},
  year = {2026},
  doi = {10.48550/arxiv.2602.17158}
}

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