Atomic Fisher information and entanglement forecasting for quantum system based on artificial neural network and time series model. Issue 4 (9th September 2020)

• Record Type: Journal Article
• Authors: Abdel‐khalek, S.; Alhag, Azhri; Ragab, Mahmoud; Abo‐Dahab, S. M.; Algarni, Ali; Ahmad, Hijaz
• Is Part Of: International journal of quantum chemistry. Volume 121:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-09
• Subjects: atomic Fisher information -- binomial distribution -- neural network -- quantum entropy -- time series; Quantum chemistry -- Periodicals; 541.28
• DOI: 10.1002/qua.26446
• Languages: English
• ISSNs: 0020-7608

Abstract

Abstract: 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. Abstract : (a) The plots of the Fixed Length Symmetric Filter and Frequency response function for the AFI and (b) The plots of the Hidden Layer and Output Layer for the AFI.