Entanglement detection with artificial neural networks
Naema Asif, Uman Khalid, Awais Khan, Trung Q. Duong, Hyundong Shin
Abstract
Quantum entanglement is one of the essential resources involved in quantum information processing tasks. However, its detection for usage remains a challenge. The Bell-type inequality for relative entropy of coherence serves as an entanglement witness for pure entangled states. However, it does not perform reliably for mixed entangled states. This paper constructs a classifier by employing the relationship between coherence and entanglement for supervised machine learning methods. This method encodes multiple Bell-type inequalities for the relative entropy of coherence into an artificial neural network to detect the entangled and separable states in a quantum dataset.
Topics & Concepts
Quantum entanglementEntanglement witnessComputer scienceKullback–Leibler divergenceCoherence (philosophical gambling strategy)QuantumQuantum discordArtificial neural networkEntropy (arrow of time)Quantum relative entropyArtificial intelligenceTheoretical computer scienceStatistical physicsQuantum mechanicsPhysicsQuantum Information and CryptographyQuantum Computing Algorithms and ArchitectureQuantum Mechanics and Applications