Evaluating Supervised Learning Approaches for Quantification of Quantum Entanglement
Quantum Physics
2025-12-29 v1
Abstract
Quantum entanglement is a key resource in quantum computing and quantum information processing tasks. However, its quantification remains a major challenge since it cannot be directly extracted from physical observables. To address this issue, we study a few machine-learning based models to estimate the amount of entanglement in two-qubit as well as three-qubit systems. We use measurement outcomes as the input features and entanglement measures as the training labels. Our models predict entanglement without requiring the full state information. This demonstrates the potential of machine learning as an effcient and powerful tool for characterizing quantum entanglement
Cite
@article{arxiv.2512.21893,
title = {Evaluating Supervised Learning Approaches for Quantification of Quantum Entanglement},
author = {Shruti Aggarwal and Trasha Gupta and R. K. Agrawal and S. Indu},
journal= {arXiv preprint arXiv:2512.21893},
year = {2025}
}
Comments
8 pages, 10 figures