Optimizing Software Development Lifecycles with Quantum Computing Integration

  • Authors

    • Dr. Deepak Mishra Professor, University of Lucknow, India Author
    • Dr. Kavitha Selvaraj Associate Professor, Periyar University, India Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2018PI6N4K

    Published 05-05-2018

  • Quantum Computing, Software Development Lifecycle (SDLC), Quantum Algorithms, Quantum-Classical Integration, Agile Development, Devops, Software Engineering, Quantum-Enhanced Testing, Hybrid Development Models, Future of Software Development

    Issue

    Vol. 1 No. 1 (2018)

    Section

    Articles

    How to Cite

    Optimizing Software Development Lifecycles with Quantum Computing Integration. (2018). International Journal of Modern Research in Science & Engineering, 1(1), 01-10. https://doi.org/10.67228/30716357/IJMRSE-2018PI6N4K

  • Abstract

    This paper explores the potential of integrating quantum computing into the software development lifecycle (SDLC) to enhance efficiency, speed, and problem-solving capabilities. As traditional SDLC models face increasing challenges in managing large-scale systems and complex problem-solving, quantum computing presents an innovative approach to optimize key aspects of software engineering. We discuss the basics of quantum computing and its relevant algorithms, followed by an examination of how quantum technologies can be integrated with existing SDLC models. The paper also highlights case studies and real-world use cases demonstrating the benefits of quantum-enhanced development processes. Despite the promising advantages, challenges such as quantum hardware limitations and the learning curve for developers remain. This paper concludes by proposing future directions for quantum computing in software development and its potential to revolutionize the field.

  • References

    [1] Nielsen, M. A., & Chuang, I. L. (2000). Quantum Computation and Quantum Information. Cambridge University Press.

    [2] Bernstein, E., & Vazirani, U. (1997). Quantum complexity theory. SIAM Journal on Computing, 26(5), 1411–1473.

    [3] Gay, S. J. (2006). Quantum programming languages: Survey and bibliography. Mathematical Structures in Computer Science, 16(4), 581–600.

    [4] Selinger, P. (2004). A brief survey of quantum programming languages. In Proceedings of the 7th International Symposium on Functional and Logic Programming (FLOPS 2004) (pp. 1–6). Springer.

    [5] Sofge, D. (2008). A survey of quantum programming languages: History, methods, and tools. In Second International Conference on Quantum, Nano and Micro Technologies (pp. 66–71). IEEE.

    [6] Ying, M., Feng, Y., Duan, R., Li, Y., & Yu, N. (2012). Quantum programming: From theories to implementations. Science China Physics, Mechanics and Astronomy, 57(10), 1903–1909.

    [7] Wecker, D., & Svore, K. M. (2014). LIQUi|>: A Software Design Architecture and Domain-Specific Language for Quantum Computing. Microsoft Research.

    [8] Boehm, B. (2006). A view of 20th and 21st century software engineering. In Proceedings of the 28th International Conference on Software Engineering (ICSE) (pp. 12–29).

    [9] Pressman, R. S. (2014). Software Engineering: A Practitioner's Approach (8th ed.). McGraw-Hill Education.

    [10] Sommerville, I. (2015). Software Engineering (10th ed.). Pearson Education.

    [11] Almudever, C. G., Lao, L., Fu, X., Khammassi, N., Ashraf, I., Iorga, D., et al. (2017). The engineering challenges in quantum computing. In Design, Automation and Test in Europe Conference (DATE 2017) (pp. 836–845). IEEE.

    [12] IBM Research. (2017). Qiskit: An open-source software development kit for quantum computing. IBM.

  • Downloads