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This is a Quantitative Study study.

October 16, 2025Open Access

Using Matrix-Free Tensor-Network Optimizations to Construct a Reduced-Scaling and Robust Second-Order M{}ller-Plesset Theory

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The method reduces computational complexity while maintaining accuracy in Møller-Plesset theory.
Incorporating a novel CPD solver achieves O(NR^2) scaling for order-4 tensor optimizations.
The study effectively switches between THC and CPD factorizations, enhancing efficiency.
Results demonstrate that limitations on CPD and THC ranks preserve Møller-Plesset method accuracy.

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Implication

This investigation combines canonical polyadic decomposition and tensor hyper-contraction, showing reduced computational complexity in electronic structure methods.

Cite This Study

Pierce et al. (2025) studied this question.

synapsesocial.com/papers/68f0d5eb105731330a2b1f9c https://doi.org/10.48550/arxiv.2502.17683

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Implication

This investigation combines canonical polyadic decomposition and tensor hyper-contraction, showing reduced computational complexity in electronic structure methods.

Using Matrix-Free Tensor-Network Optimizations to Construct a Reduced-Scaling and Robust Second-Order M{}ller-Plesset Theory

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Discussion

Implication

Cite This Study

Using Matrix-Free Tensor-Network Optimizations to Construct a Reduced-Scaling and Robust Second-Order M{}ller-Plesset Theory

Key Points

Discussion

Implication

Cite This Study

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