Classifying Multipartite Continuous Variable Entanglement Structures through Data-augmented Neural Networks

TL;DR

This paper introduces a data-augmented neural network approach for classifying multipartite continuous variable entanglement structures using homodyne measurement data, improving accuracy and enabling analysis of complex quantum states.

Contribution

It develops a quantum data augmentation method combining classical processing and quantum principles to enhance neural network performance in entanglement classification.

Findings

Network accurately classifies entanglement structures in tripartite and quadripartite states.

Data augmentation significantly improves classification accuracy.

Method extends machine learning applications to complex quantum systems in large Hilbert spaces.

Abstract

Neural networks have emerged as a promising paradigm for quantum information processing, yet they confront the challenge of generating training datasets with sufficient size and rich diversity, which is particularly acute when dealing with multipartite quantum systems. For instance, in the task of classifying different structures of multipartite entanglement in continuous variable systems, it is necessary to simulate a large number of infinite-dimension state data that can cover as many types of non-Gaussian states as possible. Here, we develop a data-augmented neural network to complete this task with homodyne measurement data. A quantum data augmentation method based on classical data processing techniques and quantum physical principles is proposed to efficiently enhance the network performance. By testing on randomly generated tripartite and quadripartite states, we demonstrate that the network can indicate the entanglement structure among the various partitions and the accuracies are significantly improved with data augmentation. Our approach allows us to further extend the use of data-driven machine learning techniques to more complex tasks of learning quantum systems encoded in a large Hilbert space.