Modal Backflow Neural Quantum States for Anharmonic Vibrational Calculations
Lexin Ding, Markus Reiher

TL;DR
This paper introduces a novel neural quantum state architecture called modal backflow (MBF) for anharmonic vibrational calculations, achieving spectroscopically accurate energies and transitions.
Contribution
The paper develops the MBF neural quantum state design for bosonic systems, enabling efficient and accurate anharmonic vibrational calculations with a new selected-configuration evaluation scheme.
Findings
MBF network delivers accurate zero-point energies.
Achieves precise vibrational transition predictions.
Effective in both artificial and ab initio Hamiltonians.
Abstract
Neural quantum states (NQS) are a promising ansatz for solving many-body quantum problems due to their inherent expressiveness. Yet, this expressiveness can only be harnessed efficiently for treating identical particles if the suitable physical knowledge is hardwired into the neural network itself. For electronic structure, NQS based on backflow determinants has been shown to be a powerful ansatz for capturing strong correlation. By contrast, the analogue for bosons, backflow permanents, is unpractical due to the steep cost of computing the matrix permanent and due to the lack of particle conservation in common bosonic problems. To circumvent these obstacles, we introduce a modal backflow (MBF) NQS design and demonstrate its efficacy by solving the anharmonic vibrational problem. To accommodate the demand of high accuracy in spectroscopic calculations, we implement a…
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