Detecting genuine multipartite entanglement via machine learning

Abstract

In recent years, supervised and semi-supervised machine learning methods such as neural networks, support vector machines (SVM), and semi-supervised support vector machines (S4VM) have been widely used in quantum entanglement and quantum steering verification problems. However, few studies have focused on detecting genuine multipartite entanglement based on machine learning. Here, we investigate supervised and semi-supervised machine learning for detecting genuine multipartite entanglement of three-qubit states. We randomly generate three-qubit density matrices, and train an SVM for the detection of genuine multipartite entangled states. Moreover, we improve the training method of S4VM, which optimizes the grouping of prediction samples and then performs iterative predictions. Through numerical simulation, it is confirmed that this method can significantly improve the prediction accuracy.

arXiv comment9 pages, 8 figures

References in corpus (49)

• Quantum entanglement
• Entanglement of Formation of an Arbitrary State of Two Qubits
• Mixed State Entanglement and Quantum Error Correction
• Separability Criterion for Density Matrices
• A computable measure of entanglement
• Separability of Mixed States: Necessary and Sufficient Conditions
• Three qubits can be entangled in two inequivalent ways
• Entanglement of a Pair of Quantum Bits
• Entanglement detection
• On the volume of the set of mixed entangled states
• Scalable multi-particle entanglement of trapped ions
• Universal state inversion and concurrence in arbitrary dimensions
• Geometric measure of entanglement and applications to bipartite and multipartite quantum states
• Four qubits can be entangled in nine different ways
• Classification of mixed three-qubit states
• Characterizing Entanglement
• Quantum steering: a review with focus on semidefinite programming
• Entanglement Certification $-$ From Theory to Experiment
• Taming multiparticle entanglement
• Separability criteria for genuine multiparticle entanglement
• Separability and distillability of multiparticle quantum systems
• Detection of high-dimensional genuine multi-partite entanglement of mixed states
• Monotones and invariants for multi-particle quantum states
• Characterizing the entanglement of symmetric many-particle spin-1/2 systems
• Entanglement-Based Machine Learning on a Quantum Computer
• Quantum Entanglement in Deep Learning Architectures
• Convex-roof extended negativity as an entanglement measure for bipartite quantum systems
• Variational Characterisations of Separability and Entanglement of Formation
• Detecting genuine multipartite quantum non-locality -- a simple approach and generalization to arbitrary dimension
• Family of Concurrence Monotones and its Applications
• Machine Learning ${\mathbb{Z}}_2$ Quantum Spin Liquids with Quasi-particle Statistics
• A Separability-Entanglement Classifier via Machine Learning
• Multipartite entanglement detection from correlation tensors
• Witnessing genuine multipartite entanglement with positive maps
• Entanglement criteria for Dicke states
• Machine learning non-local correlations
• Analytical characterization of the genuine multiparticle negativity
• Roofs and Convexity
• Cost of quantum entanglement simplified
• Quantify entanglement by concurrence hierarchy
• Quantum phase recognition via unsupervised machine learning
• Concurrence in arbitrary dimensions
• Measures of entanglement in multipartite bound entangled states
• Experimentally accessible lower bounds for genuine multipartite entanglement and coherence measures
• Steerability detection of arbitrary 2-qubit state via machine learning
• Einstein-Podolsky-Rosen steering based on semi-supervised machine learning
• Entanglement Verification with Deep Semi-supervised Machine Learning
• The classification of multipartite quantum correlation
• Quantum variational learning for entanglement witnessing

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• Supervised Learning Guarantee for Quantum AdaBoost
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• Variable Hyperparameterized Gaussian Kernel using Displaced Squeezed Vacuum State
• Threetangle in the XY-model class with a non-integrable field background