A quantum-inspired classification for random mixed states

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.