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Multiclass Classification of Metrologically Resourceful Tripartite Quantum States with Deep Neural Networks.

Syed Muhammad Abuzar Rizvi1, Naema Asif1, Muhammad Shohibul Ulum1

  • 1Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Korea.

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Summary

Artificial neural networks effectively classify tripartite quantum entanglement, distinguishing between fully separable, biseparable, and fully entangled states. This method offers efficient and generalized entanglement detection for quantum technologies.

Keywords:
Heisenberg limitartificial neural networksdeep neural networksmulticlass classificationquantum entanglementquantum metrologyquantum sensing

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Area of Science:

  • Quantum mechanics
  • Quantum information science
  • Artificial intelligence

Background:

  • Quantum entanglement is crucial for quantum computing, communication, and sensing.
  • Entangled probe states enhance precision in quantum sensing and metrology.
  • Noise can degrade entangled states, necessitating robust classification methods.

Purpose of the Study:

  • To develop an effective method for detecting and classifying tripartite entanglement in quantum systems.
  • To address limitations of current mathematical methods in classifying multiclass entanglement, especially in mixed states.
  • To explore the utility of artificial neural networks (ANNs) for entanglement classification.

Main Methods:

  • Utilized deep neural networks (DNNs) for multiclass classification of tripartite quantum states.
  • Trained the DNN using a dataset of tripartite quantum states based on Bell's inequality.
  • Classified states into three categories: fully separable, biseparable, and fully entangled.

Main Results:

  • The ANN model successfully classified tripartite quantum states with high accuracy.
  • The method demonstrated computational efficiency through a small number of required measurements.
  • The approach maintained generalization capabilities across a large Hilbert space of tripartite states.

Conclusions:

  • Artificial neural networks provide a powerful tool for classifying tripartite entanglement.
  • This method enhances the reliability of quantum sensing and metrology by ensuring probe state quality.
  • The computationally efficient and generalized approach supports advancements in quantum information processing.