An Optimized Quantum Computing Integration Approach for Multi-Cloud Environments: Architecture Design for Efficiency, Security, and Intelligent Workload Management

Authors

  • Dr. Natsuki Ono Graduate School of Informatics, Kagawa University, Takamatsu, Japan Author
  • Dr. Riku Ando Faculty of Engineering, Saga University, Saga, Japan Author

Keywords:

Quantum Computing, Multi-Cloud Architecture, Hybrid Computing, Systems

Abstract

The integration of quantum computing into multi-cloud environments represents a transformative paradigm in distributed computing, offering substantial improvements in computational efficiency, security resilience, and adaptive workload orchestration. However, the heterogeneous nature of cloud infrastructures, combined with the nascent and resource-sensitive characteristics of quantum systems, introduces significant architectural, operational, and security challenges. This paper proposes an optimized quantum computing integration approach designed specifically for multi-cloud ecosystems, emphasizing scalable architecture design, intelligent workload distribution, and enhanced cryptographic security mechanisms.
The study synthesizes foundational quantum computing principles with contemporary multi-cloud orchestration strategies to develop a hybrid architectural framework. The proposed model leverages quantum-enhanced optimization techniques, including quantum approximate optimization principles, to dynamically allocate computational tasks across distributed cloud nodes. Additionally, it integrates quantum-safe security protocols to mitigate emerging cyber threats in cloud environments. Prior research highlights the relevance of such integration, particularly in solving complex optimization problems and enhancing system-level security (Brown & White, 2022; Cao et al., 2020).
A structured literature synthesis reveals that while cloud computing and quantum computing have individually matured, their convergence remains underexplored in practical deployment contexts. This paper addresses this gap by introducing an architecture that supports adaptive workload balancing, quantum-classical hybrid execution layers, and secure inter-cloud communication channels. The findings indicate that quantum-enhanced orchestration can significantly reduce computational latency and improve resource utilization efficiency in multi-cloud systems.

Downloads

Download data is not yet available.

References

1. Bernstein, D. J., Curtis, R., Heninger, N., Lange, T., & van Someren, N. (2017). Quantum algorithms for cloud security. Journal of Cloud Computing, 6(1), 23.

2. Bernstein, D. J., Curtis, R., Heninger, N., Lange, T., & van Someren, N. (2017). Quantum algorithms for cloud security. Journal of Cloud Computing, 6(1), 23.

3. Brown, A., & White, R. (2022). Optimization Problems and the Quantum Approximate Optimization Algorithm (QAOA). International Journal of Quantum Computing, 8(2), 101-118.

4. Cao, Y., Guerreschi, G. G., & Aspuru-Guzik, A. (2020). Quantum cloud computing: A hybrid approach for solving industry problems. Nature Reviews Physics, 2(1), 1-12.

5. Cao, Y., Guerreschi, G. G., & Aspuru-Guzik, A. (2020). Quantum cloud computing: A hybrid approach for solving industry problems. Nature Reviews Physics, 2(1), 1-12.

6. Davis, K., & Martinez, L. (2021). Challenges in Quantum Computing Integration with Cloud Systems. Journal of Emerging Technologies, 9(3), 200-215.

7. Johnson, P., & Lee, C. (2023). Multi-Cloud Architectures and Quantum Computing Integration. Cloud Computing Review, 15(1), 45-60.

8. Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation and Quantum Information: 10th Anniversary Edition. Cambridge University Press.

9. Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation and Quantum Information: 10th Anniversary Edition. Cambridge University Press.

10. Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation and Quantum Information: 10th Anniversary Edition. Cambridge University Press.

11. Petcu, D., Di Martino, B., Venticinque, S., Rak, M., Lopez, G. E., Cossu, R., & Máhr, T. (2013). Experiences in building a mosaic of clouds. Journal of Cloud Computing: Advances, Systems and Applications, 2(1), 12.

12. Petcu, D., Di Martino, B., Venticinque, S., Rak, M., Lopez, G. E., Cossu, R., & Máhr, T. (2013). Experiences in building a mosaic of clouds. Journal of Cloud Computing: Advances, Systems and Applications, 2(1), 12.

13. Petcu, D., Di Martino, B., Venticinque, S., Rak, M., Lopez, G. E., Cossu, R., & Máhr, T. (2013). Experiences in building a mosaic of clouds. Journal of Cloud Computing: Advances, Systems and Applications, 2(1), 12.

14. Patel, S., & Kim, J. (2024). Future Directions in Quantum Computing Research. Computational Advances Journal, 11(1), 75-88.

15. Smith, J. (2023). Advances in Quantum Computing: Shor’s Algorithm and Its Applications. Journal of Quantum Information Science, 12(4), 345-360.

16. Thompson, R., & Green, M. (2022). Hybrid Models of Quantum and Classical Computing. Computing Innovations, 6(2), 55-70.

17. Quantum Cloud Computing: A Review, Open Problems, and Future Directions. (n.d.). https://arxiv.org/html/2404.11420v1

Downloads

Published

2026-07-01

How to Cite

An Optimized Quantum Computing Integration Approach for Multi-Cloud Environments: Architecture Design for Efficiency, Security, and Intelligent Workload Management. (2026). European Journal of Emerging Cloud and Quantum Computing, 3(07), 1-6. https://parthenonfrontiers.com/index.php/ejecqc/article/view/639

Similar Articles

1-10 of 18

You may also start an advanced similarity search for this article.