Multiclass Classification of Metrologically Resourceful Tripartite Quantum States with Deep Neural Networks

被引:0
|
作者
Rizvi, Syed Muhammad Abuzar [1 ]
Asif, Naema [1 ]
Ulum, Muhammad Shohibul [1 ]
Duong, Trung Q. [2 ]
Shin, Hyundong [1 ]
机构
[1] Kyung Hee Univ, Dept Elect & Informat Convergence Engn, Yongin 17104, South Korea
[2] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT7 1NN, Antrim, North Ireland
基金
新加坡国家研究基金会;
关键词
quantum entanglement; quantum metrology; quantum sensing; Heisenberg limit; multiclass classification; artificial neural networks; deep neural networks; ENTANGLEMENT; INFORMATION;
D O I
10.3390/s22186767
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Quantum entanglement is a unique phenomenon of quantum mechanics, which has no classical counterpart and gives quantum systems their advantage in computing, communication, sensing, and metrology. In quantum sensing and metrology, utilizing an entangled probe state enhances the achievable precision more than its classical counterpart. Noise in the probe state preparation step can cause the system to output unentangled states, which might not be resourceful. Hence, an effective method for the detection and classification of tripartite entanglement is required at that step. However, current mathematical methods cannot robustly classify multiclass entanglement in tripartite quantum systems, especially in the case of mixed states. In this paper, we explore the utility of artificial neural networks for classifying the entanglement of tripartite quantum states into fully separable, biseparable, and fully entangled states. We employed Bell's inequality for the dataset of tripartite quantum states and train the deep neural network for multiclass classification. This entanglement classification method is computationally efficient due to using a small number of measurements. At the same time, it also maintains generalization by covering a large Hilbert space of tripartite quantum states.
引用
收藏
页数:18
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