Evaluating Supervised Learning Approaches for Quantification of Quantum Entanglement

TL;DR

This paper explores machine learning models to estimate quantum entanglement from measurement data, offering a promising approach to quantify entanglement without full state reconstruction in two- and three-qubit systems.

Contribution

It introduces supervised learning models that predict quantum entanglement measures directly from measurement outcomes, bypassing the need for complete state information.

Findings

Models accurately predict entanglement in two- and three-qubit systems

Machine learning provides an efficient alternative to traditional entanglement quantification methods

Demonstrates potential for real-time entanglement estimation in quantum experiments

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