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

Xiaoting Gao, Mingsheng Tian, Feng-Xiao Sun, Ya-Dong Wu, Yu Xiang, Qiongyi He
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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.
通过数据增强神经网络对多方连续可变纠缠结构进行分类
神经网络已成为量子信息处理的一个前景广阔的范例,然而它们面临着生成具有足够规模和丰富多样性的训练数据集的挑战,这在处理多方量子系统时尤为突出。例如,在连续可变系统中对多方纠缠的不同结构进行分类的任务中,有必要模拟大量无限维度的状态数据,以涵盖尽可能多的非高斯状态类型。在这里,我们开发了一种数据增强神经网络,利用同源测量数据完成这项任务。我们提出了一种基于经典数据处理技术和量子物理原理的量子数据增强方法,以有效提高网络性能。通过对随机生成的三方态和四方态进行测试,我们证明了该网络可以指示不同分区之间的纠缠结构,并且通过数据增强显著提高了精确度。我们的方法使我们能够进一步将数据驱动的机器学习技术扩展到学习大型希尔伯特空间中编码的量子系统的更复杂任务中。
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