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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2024-01-01
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| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/ad9f20 |
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