Deep learning the hierarchy of steering measurement settings of qubit-pair states

Abstract

Quantum steering has attracted increasing research attention because of its fundamental importance, as well as its applications in quantum information science. Here we leverage the power of the deep learning model to infer the steerability of quantum states with specific numbers of measurement settings, which form a hierarchical structure. A computational protocol consisting of iterative tests is constructed to overcome the optimization, meanwhile, generating the necessary training data. According to the responses of the well-trained models to the different physics-driven features encoding the states to be recognized, we can numerically conclude that the most compact characterization of the Alice-to-Bob steerability is Alice's regularly aligned steering ellipsoid; whereas Bob's ellipsoid is irrelevant. We have also provided an explanation to this result with the one-way stochastic local operations and classical communication. Additionally, our approach is versatile in revealing further insights into the hierarchical structure of quantum steering and detecting the hidden steerability.

14 pages, 4 figures

References in corpus (21)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Device-independent security of quantum cryptography against collective attacks
• Steering, Entanglement, Nonlocality, and the EPR Paradox
• Experimental criteria for steering and the Einstein-Podolsky-Rosen paradox
• Einstein-Podolsky-Rosen steering provides the advantage in entanglement-assisted subchannel discrimination with one-way measurements
• Joint measurability of generalized measurements implies classicality
• Is quantum advantage the right goal for quantum machine learning?
• Inverse design of 3d molecular structures with conditional generative neural networks
• Artificial Intelligence and Machine Learning for Quantum Technologies
• Quantifying Quantumness of Channels Without Entanglement
• Complete classification of steerability under local filters and its relation with measurement incompatibility
• Quantum steerability: characterization, quantification, superactivation and unbounded amplification
• Super-Activation of Quantum Steering
• Experimental characterisation of a non-Markovian quantum process
• Quantum correlations of two-qubit states with one maximally mixed marginal
• Einstein-Podolsky-Rosen Steering and Quantum Steering Ellipsoids: Optimal Two-Qubit States and Projective Measurements
• Building separable approximations for quantum states via neural networks
• Experimental hierarchy and optimal robustness of quantum correlations of two-qubit states with controllable white noise
• Detected the steerability bounds of the generalized Werner states via BackPropagation neural network
• Characterisation and fundamental limitations of irreversible stochastic steering distillation
• On the power of one pure steered state for EPR-steering with a pair of qubits

Cited by in corpus (6)

• Unveiling quantum steering by quantum-classical uncertainty complementarity
• Genuine multipartite entanglement verification with convolutional neural networks
• Measuring network quantum steerability utilizing artificial neural networks
• Quantifying Quantum Steering with Limited Resources: A Semi-supervised Machine Learning Approach
• Unveiling the nonclassicality within quasi-distribution representations through deep learning
• Operational simultaneous correlations in complementary bases of bipartite states via one-sided semi-device-independent steering