Atomic Fisher information and entanglement forecasting for quantum system based on artificial neural network and time series model,International Journal of Quantum Chemistry

当前位置： X-MOL 学术 › Int. J. Quantum Chem. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Atomic Fisher information and entanglement forecasting for quantum system based on artificial neural network and time series model
International Journal of Quantum Chemistry ( IF 2.3 ) Pub Date : 2020-09-09 , DOI: 10.1002/qua.26446
S. Abdel-khalek _{1,

2} , Azhri Alhag ₂ , S. M. Abo-Dahab ₃ , Mahmoud Ragab ₄ , Hijaz Ahmad ₅

Affiliation

In this article, we apply a statistical model for forecasting the quantum entanglement between a two‐qubit and optical field in binomial distribution. We explore the link between the atomic Fisher information, quantum entropy, and the statistical properties of the field. The qubit‐qubit entanglement is investigated through concurrence during the interaction time. The dynamics of the statistical quantities will be forecasted using the time series and neural network models. The effect of the field distribution parameter (number of successes) is examined by the time series models and artificial neural network. We compare the accuracy of both modes from the perspective of the dynamic of the quantum entropy and atomic Fisher information. A statistical description for the data has been obtained and is discussed to show the statistical technique analysis the data of statistical quantities. The results obtained have several applications and are related with quantum statistics and quantum information processing.

中文翻译：

基于人工神经网络和时间序列模型的量子系统原子Fisher信息和纠缠预测

在本文中，我们将应用统计模型来预测二项式分布中两个量子位与光场之间的量子纠缠。我们探索了原子Fisher信息，量子熵和该领域的统计特性之间的联系。通过交互时间的并发来研究量子位-量子位纠缠。统计量的动态将使用时间序列和神经网络模型进行预测。时间序列模型和人工神经网络检查了场分布参数（成功次数）的影响。我们从量子熵和原子Fisher信息的动力学角度比较两种模式的准确性。已经获得了数据的统计描述，并进行了讨论，以显示统计技术对统计量数据的分析。获得的结果具有多种应用，并且与量子统计和量子信息处理有关。

更新日期：2020-09-09

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11