Analytic methods in quantum computing

Wolfgang Scherer

Alessandro Pagano, Veronica Rossano, Francesca Pia Travisani

Quantum computing (QC) is rapidly emerging as a transformative technology capable of significantly advancing learning analytics (LA). This paper investigates how QC can effectively address the challenges associated with the processing and analysis of complex educational datasets. Explore applications of quantum methods in personalized recommendation systems, student performance prediction, educational content optimization, and adaptive learning platforms. In addition, the study examines critical integration issues-including compatibility between classical and quantum frameworks, algorithmic complexity, and performance evaluation. By highlighting both advantages and limitations of quantum approaches, this research offers insight into how QC can complement and enhance current LA practices, ultimately contributing to the development of more precise, efficient, and personalized educational analytics tools. These insights may serve as a foundation for future research and practical deployments of quantum-enhanced educational analytics.

Gia Giorgadze

Preface Basic of Quantum Computation Quantum Computers Based on Exactly Solvable Models & Geometric Phases Quantum Processor Based on the Three-Level Quantum System Methods of Geometric Control Theory for Quantum Computations Analytic Methods in Quantum Computation References Index.

Myo Myo Aung

Arun Chandra Mudhol

This article explores the transformative potential of quantum computing in the field of business analytics. It begins with an introduction to quantum computing, explaining its fundamental principles and recent advancements. The study highlights the limitations of current business analytics methods and demonstrates how quantum computing could address these limitations by offering enhanced data processing capabilities, advanced algorithms, and solutions to complex optimization problems. A comprehensive literature review is conducted to provide context and identify gaps in the existing research. The article then outlines a research design that incorporates both real-world and simulated data, using online datasets and quantum computing frameworks for analysis. The findings reveal significant opportunities for quantum computing to revolutionize business analytics, including improved efficiency, accuracy, and the ability tosolve previously intractable problems. However, the article also addresses key challenges such as technical limitations, cost, accessibility, and integration issues. The discussion highlights emerging trends and provides strategic recommendations for businesses considering the adoption of quantum computing. The article concludes with a summary of the implications of integrating quantum computing into business analytics and reflects onfuture potential and challenges.

Stephen Fenner