
 The combined impact of radiation and convection on the heat transfer of a wavy fin is scrutinized in the present analysis. Here, the novelty of this research work is that it proposes a deterministic machine learning model known as an extreme learning machine to address the heat transfer problem of a wavy fin. The effect of radiation on convective heat transfer and the Rosseland approximation for the radiation heat exchange have been considered in the investigation. The nonlinear ordinary differential equation (ODE) is converted to its nondimensional form using the appropriate dimensionless variables. Runge-Kutta-Fehlberg technique (RKF 45) is used to solve the nondimensional ODE numerically. The roles of convection-conduction, radiation-conduction, thermal conductivity, and radiation parameters have been discussed for satisfying a prescribed temperature distribution in rectangular and wavy fins with graphical visualization. A rise in convection-conduction and radiation-conduction variables decreased the thermal distribution of both the wavy fin and rectangular fin. Further, ANSYS simulation analyzes the variation of temperature and total heat flux in both rectangular and wavy fins. The study demonstrates the effectiveness of the model selected through the obtained results, which indicate the potential of the regression model for providing an accurate prediction.

Investigation of the thermal analysis of a wavy fin with radiation impact : An application of extreme learning machine

Parameter optimization in decoy-state phase-matching quantum key distribution

International student mobility is essential for developing worldwide cooperation and educational exchange. However, the existing method of evaluating international academic qualifications, which frequently relies on Equivalency Certificates, encounters notable obstacles such as subjectivity, lengthy processing periods, and possible biases. In order to tackle these problems, this research suggests an innovative method that use quantum amplitude amplification (QAA) to transform the process of evaluating equivalency certificates for overseas students. QAA utilises the computing capabilities of quantum algorithms to effectively and precisely assess the comparability of educational qualifications across various academic systems. Our project seeks to optimise the equivalency certificate evaluation process by combining quantum computing and artificial intelligence approaches. This will provide a more dependable and unbiased framework for assessing the qualifications of international students. This article provides a detailed analysis of current approaches and proposes new algorithms based on QAA. The main objective is to outline a plan for using QAA in the assessment of equivalency certificates. The prospective ramifications of this strategy encompass enhanced clarity, decreased processing durations, and heightened precision in assessing the qualifications of international students, so promoting seamless transitions into higher education or employment in foreign nations. This paper reviews current equivalence assessment methodologies, proposes QAA-based algorithms tailored for this purpose, and outlines a roadmap for applying QAI to equivalency certificate evaluation. Beyond enhancing accuracy and efficiency, this research has the potential to promote global creativity and collaboration by improving the evaluation process for international students.

Evaluation of Equivalency Certificates for International Students using Quantum AI of Fairness and Efficiency QAA

At the intersection of quantum computing and machine learning, this review paper explores the transformative impact these technologies are having on the capabilities of data processing and analysis, far surpassing the bounds of traditional computational methods. Drawing upon an in-depth analysis of 32 seminal papers, this review delves into the interplay between quantum computing and machine learning, focusing on transcending the limitations of classical computing in advanced data processing and applications. This review emphasizes the potential of quantum-enhanced methods in enhancing cybersecurity, a critical sector that stands to benefit significantly from these advancements. The literature review, primarily leveraging Science Direct as an academic database, delves into the transformative effects of quantum technologies on machine learning, drawing insights from a diverse collection of studies and scholarly articles. While the focus is primarily on the growing significance of quantum computing in cybersecurity, the review also acknowledges the promising implications for other sectors as the field matures. Our systematic approach categorizes sources based on quantum machine learning algorithms, applications, challenges, and potential future developments, uncovering that quantum computing is increasingly being implemented in practical machine learning scenarios. The review highlights advancements in quantum-enhanced machine learning algorithms and their potential applications in sectors such as cybersecurity, emphasizing the need for industry-specific solutions while considering ethical and security concerns. By presenting an overview of the current state and projecting future directions, the paper sets a foundation for ongoing research and strategic advancement in quantum machine learning. 

pdf/machine-learning-applications-of-quantum-computing-a-review-20ud9wx5qk.pdf

Machine Learning Applications of Quantum Computing: A Review

Multi-mediated semi-quantum key distribution protocol with cyclic topology


 Twin extreme learning machine (TELM) is a powerful learning algorithm, which aims at learning two nonparallel hyperplanes for data classification. However, classical TELM algorithm becomes computationally expensive when it involves big data sets. In this paper, we devise a quantum TELM algorithm to address this issue. Specifically, we first utilize the quantum amplitude estimation algorithm to prepare the desired input states and then call the quantum linear systems of equations, which adopts block-encoding technique, to obtain the model parameters in the training process. Then we invoke the swap test to estimate the distances from a new data point to the two hyperplanes and then make a classification in the prediction stage. The final complexity analysis demonstrates that our algorithm has an exponential speedup under certain conditions over the classical counterpart.

Quantum algorithm for twin extreme learning machine

Quantum Kernel Logistic Regression Based Newton Method

Security analysis for single-state circular mediated semi-quantum key distribution


 Mediated semi-quantum key distribution (MSQKD) is a popular topic in quantum cryptography, which requires less quantum abilities from legitimate users, Alice and Bob, and has the advantage of high noise tolerance. In this paper, we propose a new MSQKD protocol where the third-party quantum server M can randomly send single-state and entangled-state without informing Alice and Bob the type of qubit. In addition, in the case where M has an infinite quantum source, we derive the key rate and noise tolerance of the protocol, which proves the unconditional security of the protocol. Furthermore, as M changes the ratio of single-state and entangled-state, the noise tolerance also changes. In particular, when M sends only single-state or entangled-state, our protocol degenerates into the previous MSQKD protocols, and we derive a higher noise tolerance by analysing the mismatch terms.

Mediated semi-quantum key distribution protocol mixing single-state and entangled-state

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Quantum algorithm for twin extreme learning machine

Quantum Kernel Logistic Regression Based Newton Method

Security analysis for single-state circular mediated semi-quantum key distribution

Mediated semi-quantum key distribution protocol mixing single-state and entangled-state