The relation of entanglement to the number of qubits and interactions between them for different graph states

Bordbar, Mahmoud; Naderi, Negar; Chamgordani, Mohammad Alimoradi

doi:10.1007/s12648-020-01755-x

Cited by 7 publications

(6 citation statements)

References 30 publications

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“…It is clear that the model has captured the problem’s physics and can understand the variations in the input parameters. The evaluation of the model is done with mean absolute error and R_squared ( Chamgordani, 2022 ; Bordbar, Naderi & Alimoradi Chamgordani, 2021 ; El Jery et al, 2023 ; El Jery et al, 2023 ). This COD removal model was able to achieve an MAE of 1.12%, and its is 0.99.…”

Section: Discussionmentioning

confidence: 99%

Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system’s performance by artificial neural network

El Jery,

Salman,

Al-Ansari

et al. 2023

PeerJ

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The alarming pace of environmental degradation necessitates the treatment of wastewater from the oil industry in order to ensure the long-term sustainability of human civilization. Electrocoagulation has emerged as a promising method for optimizing the removal of chemical oxygen demand (COD) from wastewater obtained from oil refineries. Therefore, in this study, electrocoagulation was experimentally investigated, and a single-factorial approach was employed to identify the optimal conditions, taking into account various parameters such as current density, pH, COD concentration, electrode surface area, and NaCl concentration. The experimental findings revealed that the most favorable conditions for COD removal were determined to be 24 mA/cm2 for current density, pH 8, a COD concentration of 500 mg/l, an electrode surface area of 25.26 cm2, and a NaCl concentration of 0.5 g/l. Correlation equations were proposed to describe the relationship between COD removal and the aforementioned parameters, and double-factorial models were examined to analyze the impact of COD removal over time. The most favorable outcomes were observed after a reaction time of 20 min. Furthermore, an artificial neural network model was developed based on the experimental data to predict COD removal from wastewater generated by the oil industry. The model exhibited a mean absolute error (MAE) of 1.12% and a coefficient of determination (R2) of 0.99, indicating its high accuracy. These findings suggest that machine learning-based models have the potential to effectively predict COD removal and may even serve as viable alternatives to traditional experimental and numerical techniques.

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Section: Discussionmentioning

confidence: 99%

Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system’s performance by artificial neural network

El Jery,

Salman,

Al-Ansari

et al. 2023

PeerJ

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“…量子关联 [1−8] 作为量子计算和量子信息中一种非常重要的物理资源, 通过对量子关联的研究, 能更清楚地刻画蕴含在量子态之间的非经典关联特性, 以及复合量子系统各子系统之间的关联程度. 量子纠缠作为量子关联中的重要组成部分, 首先, 关于量子纠缠态的判别问题已经提出了许多有用的纠缠判据: 约化判据 [9] 、重排判据 [10] 、控制判据 [11] 、纠缠Witness判据 [12] 等; 其次, 在刻画量子态之间的纠缠程度方面, 提出了许多纠缠测度: 形成纠缠测度 [13] 、concurrence纠缠测度 [14] 、相对熵纠缠测度 [15] 等. 但量子关联远不止纠缠, 为了更好地刻画量子态之间的关联程度, 已经提出了许多定义良好的关联测度, 如累积关联测度 [16] , 量子失协关联测度 [17] , 基于Tsallis熵的量子关联测度 [18] 等.…”

Section: 引言unclassified

Quantum correlation measure based on min relative entropy for two-partition and k-partition

Li¹,

Huang²,

Cui³

et al. 2023

Acta Phys. Sin.

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As a peculiar resource of quantum mechanics, quantum correlation has been applied in many aspects. In quantum information processing and quantum computing, quantum correlation plays an extremely important role, quantum correlation has been the subject of deep studies, principally due to the general belief that they are fundamental resources for different quantum information processing tasks. In addition, correlation measure is a very important physical quantity to study quantum correlation. A well-defined correlation measure needs to have some necessary properties. By proving these necessary properties, we can deepen our understanding of correlation measure. As one of the key concepts of quantum information theory, relative entropy is always used to measure the uncertainty contained in the state of physical system. In order to better understand the properties and applications of correlation measure based on relative entropy, in this paper, by the properties of the min relative entropy, we give the quantum correlation measure based on min relative entropy for two-partition and k-partition. Furthermore, we proved that it satisfies some necessary properties of quantum correlation measures, including: the nonnegativity, the invariance under local unitary operators, and monotonicity under completely positive trace-preserving. By proving these properties, we show that the given correlation measure is well-defined. Communication security has been paid attention to by people since ancient times. In today's society, with the continuous improvement of the degree of information, such as the internet, instant messaging and e-commerce applications are related to information security, and information security is related to the vital interests of everyone. Information encryption is one of the important methods to ensure information security. As an important way to ensure information security, quantum channel has been paid more and more attention. At the end of the paper, we introduce the concept of quantum channel, and discuss the influence of quantum channel on the correlation measure based on min relative entropy under k-partition. By proposing a new correlation measure and proving the effect quantum channel on the measure, we can better describe the uncertainty contained in the state of physical system.

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“…ANNs have several advantages over traditional modeling techniques, including their ability to handle large amounts of data, adapt to changes in the system, and learn from experience [69]. As such, ANNs have become an increasingly popular tool in wastewater treatment research, offering new insights into the complex processes involved in treating wastewater and improving the efficiency and effectiveness of treatment systems [70][71][72][73].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Experimental Investigation and Proposal of Artificial Neural Network Models of Lead and Cadmium Heavy Metal Ion Removal from Water Using Porous Nanomaterials

Jery,

Aldrdery,

Ghoudi

et al. 2023

Sustainability

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This study used porous nanomaterials MCM-41 and SBA-15, as well as their modified species, to remove lead and cadmium ions from water. We used X-ray diffraction (XRD), a scanning electron microscope (SEM), the Brunauer–Emmett–Teller (BET), and the Fourier transform infrared (FT-IR) method to investigate the characteristics of porous nanomaterials. Additionally, atomic absorption spectroscopy (AAS) measured the concentration of lead and cadmium ions. The stratigraphic analysis showed the samples’ isothermal shape to be type IV. This study investigated the amount, absorbent, pH changes, and adsorption time parameters. We observed that the adsorption efficiency of lead by the synthesized samples was higher than that of the adsorption of cadmium. Mesoporous structures also displayed increased adsorption efficiency due to the amino group. Four testing stages were conducted to determine the reproducibility of the adsorption by the synthesized samples, with the results showing no significant changes. As a result of the adsorption process, the structure of the recycled sample NH2-MCM-41 was preserved. We also used artificial neural networks (ANN) to propose predictive models based on the experimental results. The ANN models were very accurate, such that the mean absolute error (MAE) was less than 2% and the R2 was higher than 0.98.

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The relation of entanglement to the number of qubits and interactions between them for different graph states

Cited by 7 publications

References 30 publications

Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system’s performance by artificial neural network

Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system’s performance by artificial neural network

Quantum correlation measure based on min relative entropy for two-partition and k-partition

Experimental Investigation and Proposal of Artificial Neural Network Models of Lead and Cadmium Heavy Metal Ion Removal from Water Using Porous Nanomaterials

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