Journal article
Predicting the von Neumann entanglement entropy using a graph neural network
Machine learning: science and technology, Vol.6(3), p.35034
09/30/2025
DOI: 10.1088/2632-2153/adf811
Appears in UI Libraries Support Open Access
Abstract
Calculating the von Neumann entanglement entropy from experimental data is challenging due to its dependence on the complete wavefunction, forcing reliance on approximations such as classical mutual information (MI). We propose a machine learning approach using a graph neural network to predict the von Neumann entropy directly from experimentally accessible bitstrings. We test this approach on a Rydberg ladder system and achieve a mean absolute error of 3.6x10-3 when evaluating within the training range on a dataset with entropy values ranging from 0 to 1.9. The model achieves a mean absolute percentage error of 1.44% and outperforms MI-based bounds. When tested beyond the training range, the model maintains reasonable accuracy. Furthermore, we demonstrate that fine-tuning the model with small datasets significantly improves performance on data outside the original training range.
Details
- Title: Subtitle
- Predicting the von Neumann entanglement entropy using a graph neural network
- Creators
- Anas Saleh - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Machine learning: science and technology, Vol.6(3), p.35034
- DOI
- 10.1088/2632-2153/adf811
- ISSN
- 2632-2153
- eISSN
- 2632-2153
- Publisher
- IOP Publishing
- Number of pages
- 19
- Grant note
- DE-SC0010113 / Department of Energy; United States Department of Energy (DOE) DE-SC0010113 / U.S. Department of Energy (DOE); United States Department of Energy (DOE)
- Language
- English
- Date published
- 09/30/2025
- Academic Unit
- Physics and Astronomy
- Record Identifier
- 9985091364502771
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