What is it about?
This paper explores an alternative method to deterministic approaches for solving the ADC Hermitian eigenvalue problem within the framework of projector quantum Monte Carlo (PQMC). The primary focus of this study is on reducing the memory footprint and boosting the efficiency of the computations.
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Why is it important?
The optimization of memory and processing requirements for established quantum chemistry methods has long been a significant research challenge. One promising approach to address this issue is the reformulation of these methods within the projector quantum Monte Carlo (PQMC) framework. Previously, we introduced the quantum Monte Carlo algebraic diagrammatic construction (QMCADC). In our current work, we enhance this method by developing a scheme that combines the benefits of both stochastic and deterministic calculations. We also integrate a filtering mechanism based on importance ranking criteria, which effectively reduces the variational space and allows for tunable accuracy. Our approach significantly improves the method's efficiency. While our work is specific to algebraic diagrammatic construction (ADC), the scheme can be adapted to other PQMC methods, such as FCIQMC.
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This page is a summary of: Dense-sparse quantum Monte Carlo algebraic diagrammatic construction and importance ranking, The Journal of Chemical Physics, May 2024, American Institute of Physics,
DOI: 10.1063/5.0209137.
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