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Trapped Ion Quantum Computing Quantum Machine Learning

Evaluating Supervised Learning Approaches for Quantification of Quantum Entanglement

arXiv
Authors: Shruti Aggarwal, Trasha Gupta, R. K. Agrawal, S. Indu

Year

2025

Paper ID

36215

Status

Preprint

Abstract Read

~2 min

Abstract Words

96

Citations

N/A

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

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2025 reference point for readers tracking recent quantum research.
  • Quantum entanglement is a key resource in quantum computing and quantum information processing tasks.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2512.21893 [2] arXiv https://arxiv.org/abs/2512.21893 [3] Publisher https://arxiv.org/abs/2512.21893

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