A Benchmarking Framework for Hybrid Quantum–Classical Edge-Cloud Computing Systems
Guoxing Yao, Lav Gupta · Applied Sciences · 2025
Quantum computers are emerging as a major tool in the computation field, leveraging the principles of quantum mechanics to solve specific problems currently beyond the capability of classical computers. This technology holds significant promise in edge-main cloud deployments, where it can enable low-latency data processing and secure communication. This paper aims to establish a research foundation by integrating quantum computing with classical edge-cloud environments to promote performance across a range of applications that scientists are actively investigating. However, the successful deployment of hybrid quantum–classical edge-clouds requires a comprehensive evaluation framework to ensure their alignment with the performance requirements. This study first proposes a novel quantum benchmarking framework, including two distinct methods to evaluate latency scores based on the quantum transpilation levels across different quantum-edge-cloud platforms. The framework is then validated for the edge-cloud environment by benchmarking several well-known and useful quantum algorithms potentially useful in this domain, including Shor’s, Grover’s, and the Quantum Walks algorithm. An optimal transpilation level is eventually suggested to achieve maximum performance in quantum-edge-cloud environments. In summary, this research paper provides critical insights into the current and prospective capabilities of QPU integration, offering a novel benchmarking framework and providing a comprehensive assessment of their potential to enhance edge-cloud performance under varying parameters, including fidelity and transpilation levels.