Mixed State Entanglement Classification using Artificial Neural Networks

Abstract

Reliable methods for the classification and quantification of quantum entanglement are fundamental to understanding its exploitation in quantum technologies. One such method, known as Separable Neural Network Quantum States (SNNS), employs a neural network inspired parameterisation of quantum states whose entanglement properties are explicitly programmable. Combined with generative machine learning methods, this ansatz allows for the study of very specific forms of entanglement which can be used to infer/measure entanglement properties of target quantum states. In this work, we extend the use of SNNS to mixed, multipartite states, providing a versatile and efficient tool for the investigation of intricately entangled quantum systems. We illustrate the effectiveness of our method through a number of examples, such as the computation of novel tripartite entanglement measures, and the approximation of ultimate upper bounds for qudit channel capacities.

arXiv comment14 pages, 7 figures

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• Efficient quantum state tomography with convolutional neural networks
• Building separable approximations for quantum states via neural networks
• Sample-efficient estimation of entanglement entropy through supervised learning
• Neural Network Approach to the Simulation of Entangled States with One Bit of Communication
• Entanglement classification and \emph{non-k}-separability certification via Greenberger-Horne-Zeilinger-class fidelity
• Estimation of the geometric measure of entanglement with Wehrl Moments through Artificial Neural Networks
• Seeding neural network quantum states with tensor network states
• Detecting Markovianity of Quantum Processes via Recurrent Neural Networks
• Trade-off between Bagging and Boosting for quantum separability-entanglement classification
• Neural quantum states for entanglement depth certification from randomized Pauli measurements