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Quantum Machine Learning Quantum Simulation

Quantum Many-body Calculations Using Artificial Neural Networks

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Authors: Dong-Hee KIM

Year

2024

Paper ID

11539

Status

Peer-reviewed

Abstract Read

~2 min

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Abstract

Recent rapid development in the neural network architecture for machine learning and AI has been inspiring a new tool for fundamental physics research that is not even data-driven. In this article, I review one of such examples, called neural-network quantum states. Basic concepts, some of promising applications, and recent progresses for performance and wider applicability are briefly introduced, highlighting its potential as a highly competitive tool for quantum many-body simulations.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2024 reference point for readers tracking recent quantum research.
Recent rapid development in the neural network architecture for machine learning and AI has been inspiring a new tool for fundamental physics research that is not even data-driven.

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References & Citation Signals

[1] DOI https://doi.org/10.3938/phit.33.036 [2] Publisher https://doi.org/10.3938/phit.33.036

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External citation index: OpenAlex citation signal • updated 2026-06-15 01:49:35

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