Quantum and Quantum-Inspired Approaches in DevOps: A Systematic Review of CI/CD Acceleration Techniques
Keywords:
Quantum computing, Quantum-inspired algorithms, DevOps, CI/CD pipelines, Software delivery automationAbstract
The performance constraints of conventional DevOps and CI/CD pipelines have been exceeded by the increasing complexity of software systems and the desire for fast, reliable releases. Important DevOps activities including optimising builds, scheduling tests, resolving dependencies, and allocating resources are being investigated as possible accelerators by emerging quantum computing and quantum-inspired approaches. With a particular emphasis on speeding up CI/CD processes, this comprehensive overview analyses recent studies that have investigated the use of quantum and quantum-inspired methods in DevOps. Findings from recent white papers, conference proceedings, and peer-reviewed journals are included in this review. It classifies previous work into primary domains such as quantum optimisation for pipeline scheduling, quantum annealing for combinatorial testing, hybrid classical-quantum models for deployment choices, and quantum-inspired heuristics usable on classical hardware. Most methods are still experimental or hybrid, and the research notes that although some have shown performance benefits, others have practical limits and are not yet ready. Also covered are important issues like skills shortages, algorithm scalability, hardware accessibility, and interface with current DevOps toolchains. Although completely quantum CI/CD pipelines are still in the future, the research finds that quantum-inspired methodologies may increase pipeline efficiency and decision-making in the near term, providing a feasible means to accelerate next-generation DevOps.
References
Albash, T., & Lidar, D. A. (2018). Adiabatic quantum computation. Reviews of Modern Physics, 90(1), 015002. https://doi.org/10.1103/RevModPhys.90.015002
Aleti, A., Buhnova, B., Grunske, L., Koziolek, A., & Meedeniya, I. (2012). Software architecture optimization methods: A systematic literature review. IEEE Transactions on Software Engineering, 39(5), 658–683. https://doi.org/10.1109/TSE.2012.64
Ali, S., Briand, L. C., Hemmati, H., & Panesar-Walawege, R. K. (2009). A systematic review of the application and empirical investigation of search-based test case generation. IEEE Transactions on Software Engineering, 36(6), 742–762. https://doi.org/10.1109/TSE.2009.52
Alvarez-Alvarado, M. S., Alban-Chacón, F. E., Lamilla-Rubio, E. A., Rodríguez-Gallegos, C. D., & Velásquez, W. (2021). Three novel quantum-inspired swarm optimization algorithms using different bounded potential fields. Scientific Reports, 11(1), 11655. https://doi.org/10.1038/s41598-021-91166-9
Apolloni, B., Carvalho, C., & De Falco, D. (1989). Quantum stochastic optimization. Stochastic Processes and Their Applications, 33(2), 233–244. https://doi.org/10.1016/0304-4149(89)90037-0
Apolloni, B., Cesa-Bianchi, N., & De Falco, D. (1990). A numerical implementation of quantum annealing. In Stochastic Processes, Physics and Geometry: Proceedings of the Ascona–Locarno Conference (pp. 97–111). World Scientific.
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2022 International Journal of Engineering Science and Humanities

This work is licensed under a Creative Commons Attribution 4.0 International License.


