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Detecting entanglement in high-spin quantum systems via a stacking ensemble of machine learning models
M Y Abd-Rabbou1,2, Ahmed A Zahia3, Amr M Abdallah4
1School of Physics, University of Chinese Academy of Sciences, Yuquan Road 19A, Beijing, 100049, China.
Scientific Reports
|June 19, 2026
Summary
Ensemble machine learning accurately estimates quantum entanglement in high-spin systems. This scalable framework addresses computational challenges in high-dimensional quantum physics, offering a robust tool for entanglement quantification.
Area of Science:
- Quantum Physics
- Computational Science
- Machine Learning
Background:
- Quantifying quantum entanglement in complex systems is computationally demanding.
- High-dimensional machine learning is crucial for advancing quantum physics research.
Purpose of the Study:
- To develop a scalable machine learning framework for estimating quantum entanglement.
- To investigate ensemble machine learning for high-spin quantum systems.
Main Methods:
- Constructed a stacked ensemble regressor using Neural Networks, XGBoost, and Extra Trees.
- Trained the model on pure and mixed Werner states for dimensions up to 5.
- Utilized CatBoost as a meta-learner for enhanced predictive accuracy.
Main Results:
- The ensemble model achieved high predictive accuracy with negligible run-to-run variance.
- Identified heteroscedastic error structure, with highest fidelity near specific parameter regimes.
- Derived empirical scaling laws for training time and memory, highlighting dimensionality limitations.
Conclusions:
- Ensemble learning offers a robust and trustworthy method for characterizing quantum entanglement.
- The study provides a formula linking dataset size to system dimensionality and accuracy.
- Machine learning is essential for tackling challenges in high-dimensional quantum physics.
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