Dense-sparse quantum Monte Carlo algebraic diagrammatic construction and importance ranking

• Verfasst von: Kulahlioglu, Adem Halil [VerfasserIn]
• Verfasst von: Dreuw, Andreas [VerfasserIn]
• Titel: Dense-sparse quantum Monte Carlo algebraic diagrammatic construction and importance ranking
• Verf.angabe: Adem Halil Kulahlioglu and Andreas Dreuw
• E-Jahr: 2024
• Jahr: 28 May 2024
• Umfang: 18 S.
• Illustrationen: Illustrationen
• Fussnoten: Online veröffentlicht: 24. Mai 2024 ; Gesehen am 14.11.2024
• Titel Quelle: Enthalten in: The journal of chemical physics
• Ort Quelle: Melville, NY : American Institute of Physics, 1933
• Jahr Quelle: 2024
• Band/Heft Quelle: 160(2024), 20 vom: Mai, Artikel-ID 204111, Seite 1-18
• ISSN Quelle: 1089-7690
• DOI: doi:10.1063/5.0209137
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1908543132

Abstract

Quantum Monte Carlo Algebraic Diagrammatic Construction (QMCADC) has been proposed as a reformulation of the second-order ADC scheme for the polarization propagator within the projection quantum Monte Carlo formalism. Dense-sparse partitioning and importance ranking filtering strategies are now exploited to accelerate its convergence and to alleviate the sign problem inherent in such calculations. By splitting the configuration space into dense and sparse subsets, the corresponding projection operator is decomposed into four distinct blocks. Deterministic calculations handle the dense-to-dense and sparse-to-dense blocks, while the remaining blocks, dense-to-sparse and sparse-to-sparse, are stochastically evaluated. The dense set is efficiently stored in a fixed-size array, and the sparse set is represented through conventional floating random Monte Carlo walks. The stochastic projection is further refined through importance ranking criteria, enabling a reduction in the required number of walkers with a controllable bias. Our results demonstrate the integration of dense-sparse partitioning with importance ranking filtering to significantly enhance the efficiency of QMCADC, enabling large-scale molecular excited-state calculations. Furthermore, this novel approach maximizes the utilization of the sparsity of ADC(2), transforming QMCADC into a tailored framework for ADC calculations.