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Entanglement detection with artificial neural networks.

Naema Asif1, Uman Khalid1, Awais Khan1

  • 1Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin, Republic of Korea.

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|January 29, 2023
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Summary
This summary is machine-generated.

Detecting quantum entanglement is crucial but challenging. This study uses machine learning and coherence-based inequalities to reliably identify entangled states in quantum datasets.

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Area of Science:

  • Quantum Information Science
  • Quantum Computing
  • Machine Learning

Background:

  • Quantum entanglement is a key resource for quantum information processing.
  • Detecting entanglement, especially in mixed states, is a significant challenge.
  • Existing entanglement witnesses, like Bell-type inequalities for relative entropy of coherence, are unreliable for mixed states.

Purpose of the Study:

  • To develop a robust method for detecting quantum entanglement in both pure and mixed states.
  • To leverage the relationship between quantum coherence and entanglement for improved detection.
  • To apply supervised machine learning for classifying entangled and separable quantum states.

Main Methods:

  • Constructed a classifier using supervised machine learning.
  • Encoded multiple Bell-type inequalities for relative entropy of coherence.
  • Utilized an artificial neural network to process the encoded inequalities.
  • Trained the model on a quantum dataset containing entangled and separable states.

Main Results:

  • The developed classifier reliably detects entangled states, overcoming limitations of previous methods for mixed states.
  • The machine learning approach effectively distinguishes between entangled and separable states.
  • Demonstrated the efficacy of using coherence-based inequalities within a neural network framework.

Conclusions:

  • Supervised machine learning offers a powerful and reliable approach to entanglement detection.
  • The proposed method enhances the practical usability of quantum entanglement in information processing.
  • This work provides a valuable tool for analyzing quantum states and resources.