An Investigation of Quantum-Based Optimization Techniques for Enhancing University Admission Processes: A Case Study of University of Maiduguri, Borno State
- Project Research
- 1-5 Chapters
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- Table of Content: Available
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- NGN 5000
Background of the Study
University admission processes involve analyzing large volumes of applicant data to ensure fair and efficient candidate selection. At the University of Maiduguri in Borno State, the complexity of the admission process is compounded by manual data handling and classical computing limitations. Quantum-based optimization techniques, with their ability to process massive datasets simultaneously, offer a promising alternative to streamline admission workflows (Ibrahim, 2024; Adekunle, 2023). These techniques can enhance data analysis, reduce processing time, and improve decision-making accuracy by identifying optimal candidate selections based on multiple parameters. This study explores the integration of quantum optimization algorithms into the university admission process to achieve more objective, efficient, and transparent outcomes.
Statement of the Problem
The current admission system at the University of Maiduguri is plagued by inefficiencies due to the heavy reliance on classical data processing methods, leading to prolonged processing times and potential bias in candidate selection (Emeka, 2023). These limitations compromise the fairness and transparency of the admission process. The lack of advanced optimization tools restricts the system’s ability to handle high-dimensional data and efficiently manage the increasing volume of applications. This study seeks to investigate quantum-based optimization as a solution to these challenges, examining its feasibility, integration hurdles, and potential benefits for improving the admission process.
Objectives of the Study
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Assess quantum optimization techniques for admissions.
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Identify inefficiencies in the current process.
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Propose a framework for integrating quantum solutions into admission systems.
Research Questions
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How can quantum optimization improve admission processing?
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What inefficiencies exist in current systems?
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What is the optimal framework for integration?
Significance of the Study
This study is significant as it offers an innovative approach to modernize university admissions using quantum computing. Enhanced efficiency and fairness in candidate selection will foster greater transparency and improve institutional reputation. The findings will provide valuable insights for policy makers and academic administrators seeking to leverage advanced technologies for process optimization (Chinwe, 2024).
Scope and Limitations of the Study
The study is limited to the University of Maiduguri in Borno State, focusing on the admission process, defined objectives, and selected LGAs in the sampled state only.
Definitions of Terms
• Quantum Optimization: Techniques using quantum computing to solve complex optimization problems.
• Admission Process: The procedures for selecting candidates for university entry.
• High-Dimensional Data: Complex data sets with many variables.
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