{"title":"Classifying Multipartite Continuous Variable Entanglement Structures through Data-augmented Neural Networks","authors":"Xiaoting Gao, Mingsheng Tian, Feng-Xiao Sun, Ya-Dong Wu, Yu Xiang, Qiongyi He","doi":"arxiv-2409.07909","DOIUrl":null,"url":null,"abstract":"Neural networks have emerged as a promising paradigm for quantum information\nprocessing, yet they confront the challenge of generating training datasets\nwith sufficient size and rich diversity, which is particularly acute when\ndealing with multipartite quantum systems. For instance, in the task of\nclassifying different structures of multipartite entanglement in continuous\nvariable systems, it is necessary to simulate a large number of\ninfinite-dimension state data that can cover as many types of non-Gaussian\nstates as possible. Here, we develop a data-augmented neural network to\ncomplete this task with homodyne measurement data. A quantum data augmentation\nmethod based on classical data processing techniques and quantum physical\nprinciples is proposed to efficiently enhance the network performance. By\ntesting on randomly generated tripartite and quadripartite states, we\ndemonstrate that the network can indicate the entanglement structure among the\nvarious partitions and the accuracies are significantly improved with data\naugmentation. Our approach allows us to further extend the use of data-driven\nmachine learning techniques to more complex tasks of learning quantum systems\nencoded in a large Hilbert space.","PeriodicalId":501226,"journal":{"name":"arXiv - PHYS - Quantum Physics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Quantum Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.07909","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.