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Abstract

A deep-learning approach to optimize the selection of Slater determinants in configuration interac- tion 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 wave- function. 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 computation- ally 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.