Neural-network-supported basis optimizer for the configuration interaction problem in quantum many-body clusters: Feasibility study and numerical proof

TL;DR

This paper presents a deep learning-based method to efficiently select relevant Slater determinants in configuration interaction calculations for quantum many-body systems, improving computational efficiency and basis compactness.

Contribution

The authors develop a neural network-supported basis optimizer that enhances determinant selection in CI calculations, demonstrating its effectiveness on the Anderson model.

Findings

Significant reduction in basis size without loss of accuracy.

Improved computational efficiency over traditional truncation schemes.

Applicable to various quantum many-body Hamiltonians.

Abstract

A deep-learning approach to optimize the selection of Slater determinants in configuration interaction calculations for condensed-matter quantum many-body systems is developed. We exemplify our algorithm on the discrete version of the single-impurity Anderson model with up to 299 bath sites. Employing a neural network classifier and active learning, our algorithm enhances computational efficiency by iteratively identifying the most relevant Slater determinants for the ground-state wavefunction. We benchmark our results against established methods and investigate the efficiency of our approach as compared to other basis truncation schemes. Our algorithm demonstrates a substantial improvement in the efficiency of determinant selection, yielding a more compact and computationally manageable basis without compromising accuracy. Given the straightforward application of our neural network-supported selection scheme to other model Hamiltonians of quantum many-body clusters, our algorithm can significantly advance selective configuration interaction calculations in the context of correlated condensed matter.