This content will become publicly available on December 14, 2024
 Award ID(s):
 2103842
 NSFPAR ID:
 10507817
 Publisher / Repository:
 NeurIPS
 Date Published:
 Journal Name:
 Conference on Neural Information Processing Systems (NeurIPS)
 ISSN:
 10495258
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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INTRODUCTION Solving quantum manybody problems, such as finding ground states of quantum systems, has farreaching consequences for physics, materials science, and chemistry. Classical computers have facilitated many profound advances in science and technology, but they often struggle to solve such problems. Scalable, faulttolerant quantum computers will be able to solve a broad array of quantum problems but are unlikely to be available for years to come. Meanwhile, how can we best exploit our powerful classical computers to advance our understanding of complex quantum systems? Recently, classical machine learning (ML) techniques have been adapted to investigate problems in quantum manybody physics. So far, these approaches are mostly heuristic, reflecting the general paucity of rigorous theory in ML. Although they have been shown to be effective in some intermediatesize experiments, these methods are generally not backed by convincing theoretical arguments to ensure good performance. 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