Machine learning-based compression of quantum many body physics: PCA and autoencoder representation of the vertex function

Machine learning-based compression of quantum many body physics: PCA and autoencoder representation of the vertex function

Theoretical approaches to quantum many-body physics require developing compact representations of the complexity of generic quantum states. This paper explores an interpretable data-driven approach utilizing principal component analysis (PCA) and autoencoder neural networks to compress the two-parti...

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Bibliographic Details
Main Authors: Jiawei Zang, Matija Medvidović, Dominik Kiese, Domenico Di Sante, Anirvan M Sengupta, Andrew J Millis
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:Machine Learning: Science and Technology
Subjects:
deep learning
machine learning
quantum system
many-body system
Online Access:https://doi.org/10.1088/2632-2153/ad9f20
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