Experimental Simultaneous Learning of Multiple Non-Classical Correlations

Abstract

Non-classical correlations can be regarded as resources for quantum information processing. However, the classification problem of non-classical correlations for quantum states remains a challenge, even for finite-size systems. Although there exists a set of criteria for determining individual non-classical correlations, a unified framework that is capable of simultaneously classifying multiple correlations is still missing. In this work, we experimentally explored the possibility of applying machine-learning methods for simultaneously identifying non-classical correlations. Specifically, by using partial information, we applied artificial neural networks, support vector machines, and decision trees for learning entanglement, quantum steering, and non-locality. Overall, we found that for a family of quantum states, all three approaches can achieve high accuracy for the classification problem. Moreover, the run time of the machine-learning methods to output the state label is experimentally found to be significantly less than that of state tomography.

References in corpus (21)

• Quantum entanglement
• Separability Criterion for Density Matrices
• Separability of Mixed States: Necessary and Sufficient Conditions
• Bell nonlocality
• Separability criterion and inseparable mixed states with positive partial transposition
• Quantum steering: a review with focus on semidefinite programming
• A wavelength-tunable fiber-coupled source of narrowband entangled photons
• Quantum Entanglement in Neural Network States
• Distinguishing separable and entangled states
• Computing quantum discord is NP-complete
• Quantum Loop Topography for Machine Learning
• Automated Search for new Quantum Experiments
• Hamiltonian Learning and Certification Using Quantum Resources
• Experimental quantification of asymmetric Einstein-Podolsky-Rosen steering
• Quantum Steering Ellipsoids
• Experimental Machine Learning of Quantum States
• Sufficient criterion for guaranteeing that a two-qubit state is unsteerable
• Demonstration of MultiSetting One-Way Einstein-Podolsky-Rosen Steering in Two-Qubit Systems
• A Separability-Entanglement Classifier via Machine Learning
• Einstein-Podolsky-Rosen steering and the steering ellipsoid
• Beating the Clauser-Horne-Shimony-Holt and the Svetlichny games with Optimal States

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• Building separable approximations for quantum states via neural networks
• Efficient Online Quantum Generative Adversarial Learning Algorithms with Applications
• Efficient Bipartite Entanglement Detection Scheme with a Quantum Adversarial Solver
• Einstein-Podolsky-Rosen steering based on semi-supervised machine learning
• Entanglement Verification with Deep Semi-supervised Machine Learning
• Detected the steerability bounds of the generalized Werner states via BackPropagation neural network
• Deep learning the hierarchy of steering measurement settings of qubit-pair states
• Automated Machine Learning can Classify Bound Entangled States with Tomograms
• Efficient learning of mixed-state tomography for photonic quantum walk
• Learning entanglement breakdown as a phase transition by confusion
• Learning Algebraic Models of Quantum Entanglement
• Quantifying Quantum Steering with Limited Resources: A Semi-supervised Machine Learning Approach
• Efficient Characterization of Quantum Evolutions via a Recommender System
• Experimental certification of the nonlocal advantages of quantum imaginarity
• Detection of quantum information masking via machine learning