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Go Green: Selected Configuration Interaction as a More Sustainable Alternative for High Accuracy

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arxiv 2402.13111 v3 pith:WDC7OORF submitted 2024-02-20 physics.chem-ph cond-mat.mtrl-scicond-mat.str-elnucl-thphysics.comp-ph

classification physics.chem-phcond-mat.mtrl-scicond-mat.str-elnucl-thphysics.comp-ph
keywords configurationinteractionspaceaccuracycomputationaldeterminantsenergyfraction
verification ladder T0 review T1 audit T2 compute T3 formal
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Recently, a new distributed implementation of the full configuration interaction (FCI) method has been reported [Gao et al. J. Chem Theory Comput. 2024, 20, 1185]. Thanks to a hybrid parallelization scheme, the authors were able to compute the exact energy of propane (\ce{C3H8}) in the minimal basis STO-3G. This formidable task involves handling an active space of 26 electrons in 23 orbitals or a Hilbert space of \SI{1.3d12} determinants. This is, by far, the largest FCI calculation reported to date. Here, we illustrate how, from a general point of view, selected configuration interaction (SCI) can achieve microhartree accuracy at a fraction of the computational and memory cost, via a sparse exploration of the FCI space. The present SCI calculations are performed with the \textit{Configuration Interaction using a Perturbative Selection made Iteratively} (CIPSI) algorithm, as implemented in a determinant-driven way in the \textsc{quantum package} software. The present study reinforces the common wisdom that among the exponentially large number of determinants in the FCI space, only a tiny fraction of them significantly contribute to the energy. More importantly, it demonstrates the feasibility of achieving comparable accuracy using more reasonable and sustainable computational resources, hence reducing the ever-growing carbon footprint of computational chemistry.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier

    quant-ph 2026-08 conditional novelty 6.0 of 10

    A critical review plus small exact-FCI experiments concludes that sample-based quantum diagonalization has not beaten classical selected CI and maps where, if anywhere, a quantum or generative advantage could survive.

  2. Quantum Advantage in Computational Chemistry?

    quant-ph 2025-08 conditional novelty 5.0 of 10

    Factoring in hardware overheads and error correction, the authors predict classical chemistry algorithms stay dominant for most calculations through the 2040s, while quantum phase estimation overtakes full configurati...

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