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Optimization of Quantum Annealing Techniques for Large-Scale Data Processing in Nigerian Defence Academy, Kaduna State

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  • NGN 5000

Background of the Study
The Nigerian Defence Academy (NDA) in Kaduna State plays a vital role in the country’s national defense strategy by providing military education and training to officers. The academy’s operations rely heavily on the processing and analysis of vast amounts of data, including personnel records, training programs, strategic planning, and logistical management. Traditional computational methods often struggle with the complexity and scale of these datasets, leading to slower processing times and suboptimal decision-making (Biamonte et al., 2024). Quantum annealing, a technique used in quantum computing to solve optimization problems, offers significant promise for optimizing large-scale data processing, making it possible to find solutions to complex problems more efficiently than classical methods.

The application of quantum annealing to data processing at the NDA could significantly improve the speed and accuracy of analyzing and interpreting large datasets. This study will explore the potential of quantum annealing to optimize various data processing tasks at the NDA, contributing to more efficient operations and decision-making processes.

Statement of the Problem
The Nigerian Defence Academy faces challenges in processing and analyzing large datasets in a timely and efficient manner due to the limitations of classical computing methods. These challenges hinder the academy’s ability to make quick decisions based on large volumes of data, such as personnel management, strategic planning, and logistics optimization. Quantum annealing offers a promising solution to these issues by providing a faster and more efficient way to solve complex optimization problems. However, the feasibility and effectiveness of quantum annealing for large-scale data processing at the NDA have yet to be explored.

Objectives of the Study

  1. To investigate the potential of quantum annealing for optimizing large-scale data processing tasks at the Nigerian Defence Academy.

  2. To design and implement a quantum annealing-based model for data processing at the NDA.

  3. To evaluate the performance and benefits of quantum annealing in comparison to traditional computational methods.

Research Questions

  1. How can quantum annealing be utilized to optimize data processing tasks at the Nigerian Defence Academy?

  2. What advantages does quantum annealing offer over traditional computational methods in large-scale data processing?

  3. What challenges are associated with implementing quantum annealing techniques in the NDA’s data processing operations?

Significance of the Study
This study will contribute to optimizing data processing at the Nigerian Defence Academy by exploring the use of quantum annealing techniques. The research will improve decision-making processes, strategic planning, and logistical management at the academy, enhancing its overall operational efficiency. The findings may also provide insights into the application of quantum computing in defense and military settings, benefiting other institutions facing similar data processing challenges.

Scope and Limitations of the Study
The study will focus on the optimization of large-scale data processing tasks at the Nigerian Defence Academy through quantum annealing techniques. The research will be limited to specific data processing applications at the NDA and will not explore broader applications of quantum computing within the defense sector.

Definitions of Terms

  1. Quantum Annealing: A quantum computing technique used to solve optimization problems by finding the lowest energy state of a system, often used in large-scale data processing tasks.

  2. Large-Scale Data Processing: The analysis and manipulation of vast datasets, typically requiring significant computational resources.

  3. Optimization: The process of finding the most efficient or effective solution to a problem, often in terms of time, cost, or resources.





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