Advancement in Quantum Computing: Bridging the Gap Between Theoretical Models and Practical Application


  • Mustafa Abdulhadi Ben Hmaida Higter Institute for Sciences &Technology-Misrata


Quantum Computing, Theoretical Models, Practical Applications, Interdisciplinary Approaches, Quantum Algorithms


This paper provides a comprehensive examination of the progression and challenges in the field of quantum computing, with a specific focus on bridging the theoretical foundations with practical applications. Beginning with a historical perspective, it traces the evolution of quantum computing from its conceptual roots to its current advancements, highlighting key theoretical models such as Quantum Turing Machines, Quantum Circuits, and Quantum Annealing. The methodology involves a detailed analysis of recent breakthroughs in quantum computing, utilizing case studies in areas like quantum machine learning, biochemical system simulations, and optimization problems to demonstrate the practical implementation of theoretical models. The results underscore the significant strides made in applying quantum computing to real-world problems, despite the challenges of quantum decoherence, scalability, and integration with classical computing systems. The paper discusses interdisciplinary strategies, combining insights from computer science, physics, and engineering, as crucial for overcoming these hurdles. It emphasizes the importance of collaborative research, investment in quantum hardware, advancement of quantum software, and educational initiatives to cultivate expertise in this emerging field


Bhomia, Y., Mishra, S. K., & Chudhary, R. (2019). Quantum machine learning: A theoretical overview of quantum computing applications.

Cheng, H. P., Deumens, E., Freericks, J. K., Li, C., & Sanders, B. A. (2020). Application of quantum computing to biochemical systems: A look to the future. Frontiers in chemistry, 8, 587143.

Glover, F., Kochenberger, G., & Du, Y. (2019). Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models. 4or, 17, 335-371.

Glover, F., Kochenberger, G., Hennig, R., & Du, Y. (2022). Quantum bridge analytics I: a tutorial on formulating and using QUBO models. Annals of Operations Research, 314(1), 141-183.

Hidary, J. D., & Hidary, J. D. (2019). Quantum computing: an applied approach (Vol. 1). Cham: Springer.

Martonosi, M., & Roetteler, M. (2019). Next steps in quantum computing: Computer science's role. arXiv preprint arXiv:1903.10541.

Motta, M., & Rice, J. E. (2022). Emerging quantum computing algorithms for quantum chemistry. Wiley Interdisciplinary Reviews: Computational Molecular Science, 12(3), e1580.

National Academies of Sciences, Engineering, and Medicine. (2019). Quantum computing: progress and prospects.

Pagano, A., Angelelli, M., Calvano, M., Curci, A., & Piccinno, A. (2023, December). Quantum Computing for Learning Analytics: An Overview of Challenges and Integration Strategies. In Proceedings of the 2nd International Workshop on Quantum Programming for Software Engineering (pp. 13-16).

Park, J. E., Quanz, B., Wood, S., Higgins, H., & Harishankar, R. (2020). Practical application improvement to Quantum SVM: theory to practice. arXiv preprint arXiv:2012.07725.

Resch, S., & Karpuzcu, U. R. (2019). Quantum computing: an overview across the system stack. arXiv preprint arXiv:1905.07240.

Santana García, F. (2023). Exploring Quantum Computing applications in Machine Learning: Variational Classifiers & Quantum Convolution.

Surjeet, S., Bulla, C., Arya, A., Idrees, S., Singh, G., Rao, S. G., & Shirahatti, A. (2024). A Quantum Machine Learning Approach for Bridging the Gap Between Quantum and Classical Computing. International Journal of Intelligent Systems and Applications in Engineering, 12(12s), 553-560.



How to Cite

Ben Hmaida, M. A. (2024). Advancement in Quantum Computing: Bridging the Gap Between Theoretical Models and Practical Application. Journal of Reproducible Research, 2(2), 162–171. Retrieved from