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Giuseppe Sergioli1, Carlo Cuccu2, Carla Sophie Rieger3,4

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We developed a quantum-inspired classifier to identify entanglement in mixed quantum states. This method extends previous work and offers scalable tools for characterizing quantum correlations.

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Area of Science:

  • Quantum Information Science
  • Quantum Computing
  • Quantum Machine Learning

Background:

  • Distinguishing quantum states (product, separable, entangled) is crucial in quantum information.
  • The Pretty-Good-Measurement (PGM) classifier showed promise for pure quantum states.
  • Classifying mixed quantum states presents significant challenges due to their complexity.

Purpose of the Study:

  • To extend the PGM classifier for analyzing correlations in mixed quantum states.
  • To develop a quantum-inspired framework for identifying entanglement and separability.
  • To assess the performance of this framework on random mixed quantum state ensembles.

Main Methods:

  • A quantum-inspired classification framework was developed.
  • The framework was applied to ensembles of two- and three-qubit random mixed states.
  • The method builds upon the Pretty-Good-Measurement (PGM) classifier.

Main Results:

  • The classifier successfully identified product, separable, and entangled states in mixed ensembles.
  • The framework demonstrated scalability for characterizing quantum correlations.
  • Results confirm the utility of quantum state discrimination principles for mixed states.

Conclusions:

  • Quantum-inspired learning architectures provide effective tools for mixed-state entanglement characterization.
  • The developed framework offers a scalable and physically grounded approach.
  • This method advances the study of quantum correlations in complex quantum systems.