Time-independent quantum response calculations are performed using Tensor cores. This is achieved by mapping density matrix perturbation theory onto the computational structure of a deep neural network. The main computational cost of each deep layer is dominated by tensor contractions, i.e. dense matrix-matrix multiplications, in mixed precision arithmetics which achieves close to peak performance. Quantum response calculations are demonstrated and analyzed using self-consistent charge density-functional tight-binding theory as well as coupled-perturbed Hartree-Fock theory. For linear response calculations, a novel parameter-free convergence criterion is presented that is well-suited for numerically noisy low precision floating point operations and we demonstrate a peak performance of almost 200 Tflops using the Tensor cores of two Nvidia A100 GPUs.
@article{arxiv.2203.09621,
title = {Quantum perturbation theory using Tensor cores and a deep neural network},
author = {Joshua Finkelstein and Emanuel H. Rubensson and Susan M. Mniszewski and Christian F. A. Negre and Anders M. N. Niklasson},
journal= {arXiv preprint arXiv:2203.09621},
year = {2022}
}