Hybrid Quantum-Classical Solutions for NP-Complete Graph Problems in Scalable Cloud Microservice Environments
Seda Nur Gungor, Mehmet Karaköse · IEEE Access · 2026
Cloud computing platforms increasingly rely on large-scale microservice architectures, in which hundreds of independent services interact through complex communication structures. Analyzing these interaction patterns and identifying highly connected service groups are critical for service placement strategies aimed at optimizing communication efficiency and resource utilization. Structural analysis problems of this type can often be formulated as NP-complete problems when considered on a large scale. Quantum computing is a promising paradigm that extends the capabilities of conventional computational approaches by exploiting its advantages of quantum mechanics. This study proposes a quantum–classical hybrid algorithm based on an adapted Quantum Feasibility Labeling (QFL) algorithm to identify highly interacting microservice clusters in cloud environments and support their optimal placement. Simulation results demonstrate that the proposed modular circuit architecture can represent graph feasibility relationships and support the systematic identification of clique structures in small-scale proof-of-concept cases. In addition, the SXNORR-based modular design improves circuit reusability and structural scalability compared with the monolithic circuit structures commonly used in existing quantum graph algorithms. These findings indicate that modular quantum circuit architectures can provide a promising foundation for the application of quantum computing in the analysis of complex systems, such as cloud-based microservice interaction networks.