Secure Quantum-AI and Cloud-Driven Architecture Integrating SAP and Oracle for Scalable, Intelligent, and Real-Time Banking Systems
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806007Keywords:
Quantum inspired optimization, SAP Oracle financial pipelines, Cloud based banking systems, Enterprise data flow optimization, QUBO (Quadratic Unconstrained Binary Optimization), Hybrid quantum classical heuristics, Latency reduction, Resource allocation, Throughput enhancement, Banking IT infrastructure modernizationAbstract
The financial operations of banking institutions rely on complex pipelines involving enterprise systems like SAP for transactional and financial accounting, modern databases like Oracle for data management, and increasingly cloud environments for scalability and agility. This paper investigates how quantum‑inspired optimization techniques can enhance such SAP‑Oracle financial pipelines in cloud‑based banking systems. Specifically, we propose an architecture where the data flows from SAP modules into Oracle database layers deployed in the cloud are optimized via quantum‑inspired algorithms (such as QUBO formulations, annealing‑inspired heuristics, or hybrid quantum‑classical routines) to reduce latency, enhance throughput, improve resource allocation, and minimize operational cost. A literature review shows growing interest in quantum and quantum‑inspired approaches in finance, but a gap remains for their application in enterprise financial pipelines combining SAP, Oracle and cloud. The methodology encompasses qualitative interviews with banking IT/finance managers to identify pain points and optimization opportunities, and a quantitative proof‑of‑concept experiment simulating pipeline workloads, comparing traditional optimization vs. quantum‑inspired scheduling/resource allocation. Results indicate significant improvement in throughput (transactions per second), reduction in pipeline latency, and more balanced resource utilization, with trade‑offs in complexity and required expertise. We discuss the implications for banking operations, architectural adjustments, governance, and risk. In conclusion, quantum‑inspired optimisation offers a promising path for modernizing SAP‑Oracle financial pipelines in cloud contexts, yet success depends on careful design, skilled personnel, and incremental adoption.
References
1. Lu, Y. C., Fu, C. M., Yu, L. P., Chang, Y. J., & Chang, C. R. (2024, October 8). Quantum Inspired Portfolio Optimization in the QUBO Framework. arXiv. https://arxiv.org/abs/2410.05932
2. Chiranjeevi, Y., Sugumar, R., & Tahir, S. (2024, November). Effective Classification of Ocular Disease Using Resnet-50 in Comparison with Squeezenet. In 2024 IEEE 9th International Conference on Engineering Technologies and Applied Sciences (ICETAS) (pp. 1-6). IEEE.
3. Sangannagari, S. R. (2022). THE FUTURE OF AUTOMOTIVE INNOVATION: EXPLORING THE IN-VEHICLE SOFTWARE ECOSYSTEM AND DIGITAL VEHICLE PLATFORMS. International Journal of Research and Applied Innovations, 5(4), 7355-7367.
4. “Improved financial forecasting via quantum machine learning.” (2024). Quantum Machine Intelligence, 6, Article 27. https://doi.org/10.1007/s42484 024 00157 0
5. Fujitsu. (n.d.). Quantum Inspired Financial Services Technology & Solutions. Fujitsu Global. Retrieved from https://www.fujitsu.com/global/solutions/industry/financial services/quantum inspired/
6. Classiq. (n.d.). Quantum Finance | Classiq. Retrieved from https://www.classiq.io/industries/industries finance
7. Kommineni, M., Panyaram, S., Banala, S., Vegineni, G. C., Hullurappa, M., & Sehrawat, S. K. (2025, April). Optimizing Processes and Insights: the Role of Ai Architecture in Corporate Data Management. In 2025 International Conference on Data Science and Business Systems (ICDSBS) (pp. 1-7). IEEE.
8. Infleqtion. (n.d.). Quantum for Finance. Retrieved from https://infleqtion.com/finance/
9. Gosangi, S. R. (2022). SECURITY BY DESIGN: BUILDING A COMPLIANCE-READY ORACLE EBS IDENTITY ECOSYSTEM WITH FEDERATED ACCESS AND ROLE-BASED CONTROLS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(3), 6802-6807.
10. Adigun, P. O., Oyekanmi, T. T., & Adeniyi, A. A. (2023). Simulation Prediction of Background Radiation Using Machine Learning. New Mexico Highlands University.
11. Hughes, A. G., Baker, J. S., & Radha, S. K. (2023, January 4). A Quantum Inspired Binary Optimization Algorithm for Representative Selection. arXiv. https://arxiv.org/abs/2301.01836
12. Adari, V. K. (2024). The Path to Seamless Healthcare Data Exchange: Analysis of Two Leading Interoperability Initiatives. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11472-11480.
13. Arjunan, T., Arjunan, G., & Kumar, N. J. (2025, July). Optimizing the Quantum Circuit of Quantum K-Nearest Neighbors (QKNN) Using Hybrid Gradient Descent and Golden Eagle Optimization Algorithm. In 2025 International Conference on Computing Technologies & Data Communication (ICCTDC) (pp. 1-7). IEEE.
14. Balaji, K. V., & Sugumar, R. (2023, December). Harnessing the Power of Machine Learning for Diabetes Risk Assessment: A Promising Approach. In 2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (pp. 1-6). IEEE.
15. The Quantum Insider. (2021, October 30). Five startups altering the financial sector using quantum inspired algorithms. Retrieved from https://thequantuminsider.com/2021/10/30/5 startups altering the financial sector using quantum inspired algorithms/
16. Prabaharan, G., Sankar, S. U., Anusuya, V., Deepthi, K. J., Lotus, R., & Sugumar, R. (2025). Optimized disease prediction in healthcare systems using HDBN and CAEN framework. MethodsX, 103338.
17. Joyce, S., Pasumarthi, A., & Anbalagan, B. SECURITY OF SAP SYSTEMS IN AZURE: ENHANCING SECURITY POSTURE OF SAP WORKLOADS ON AZURE–A COMPREHENSIVE REVIEW OF AZURE-NATIVE TOOLS AND PRACTICES.
18. Joseph, J. (2025). Enabling Responsible, Secure and Sustainable Healthcare AI-A Strategic Framework for Clinical and Operational Impact.
19. Thammareddi, L., Anumolu, V. R., Kotte, K. R., Marella, B. C. C., Kumar, K. A., & Bisht, J. (2025, February). Random Security Generators with Enhanced Cryptography for Cybersecurity in Financial Supply Chains. In 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT) (pp. 1173-1178). IEEE.
20. Lanka, S. (2025). ARCHITECTURAL PATTERNS FOR AI-ENABLED TRIAGE AND CRISIS PREDICTION SYSTEMS IN PUBLIC HEALTH PLATFORMS. International Journal of Research and Applied Innovations, 8(1), 11648-11662.
21. Sugumar, R. (2023, September). A Novel Approach to Diabetes Risk Assessment Using Advanced Deep Neural Networks and LSTM Networks. In 2023 International Conference on Network, Multimedia and Information Technology (NMITCON) (pp. 1-7). IEEE.
22. Chunduru, V. K., Gonepally, S., Amuda, K. K., Kumbum, P. K., & Adari, V. K. (2022). Evaluation of human information processing: An overview for human-computer interaction using the EDAS method. SOJ Materials Science & Engineering, 9(1), 1–9.
23. Habr. (n.d.). 5 Startups Altering the Financial Sector Using Quantum Inspired Algorithms / Habr. Retrieved from https://habr.com/en/articles/587434/
