A Decision-Making Framework to Recommend Quantum-Based Solutions for Fit Problem Domains with Associated Factors

Context: Quantum Computing (QC) systems exploit the characteristics of quantum mechanics to perform computationally demanding tasks more effectually than their classical counterparts. To utilize the full potential of QC systems, Quantum Software Engineering (QSE) has emerged to manage the challenges of quantum software development. Problem: Due to the lack of awareness of QC problem domains and their associated factors to consider and apply QC in the Software Engineering (SE) process, the development of a QC system becomes complex and less cost effective if without a guidance for software engineers. Objective: The proposed work aims to develop a systematic decision-making framework to help identify the quantum problem domains and prioritize the critical factors to be considered for the application of QC during the software development life cycle (SDLC). Method: The proposed method is implemented in two folds. First, a Systematic Literature Review (SLR) is performed to identify a set of QC problem domains and their associated factors that should be accounted for in the SE process. Second, an online survey is conducted to collect insights on the same aspects from practitioners in their SE processes to form another set of data. Finally, the relationships of the two sets are validated through careful mappings with the assistance of domain experts. Results: Five problem domains and six factors are classified by the proposed methodology, and some of them will benefit from a QC-based solution in the SE process. The results indicate that “Algorithm Development” and “Language Development” are two potential problem domains to apply a QC-based solution. For these two domains, “Current Hardware Limitations”, “Standardized Coding Practices” and “Software Specialization/Adaptation” are the critical factors to be considered during the SE process. Conclusion: Organizations and QSE community can take advantage of the identified problem domains and their associated factors to make decisions in the recommendation of QC-based solutions.