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.
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.
Abstract Quantum computing systems harness the power of quantum mechanics to execute computationally demanding tasks more effectively than their classical counterparts. This has led to the emergence of Quantum Software Engineering (QSE), which focuses on unlocking the full potential of quantum computing systems. As QSE gains prominence, it seeks to address the evolving challenges of quantum software development by offering comprehensive concepts, principles, and guidelines. This paper aims to identify, prioritize, and develop a systematic decision-making framework of the challenging factors associated with QSE process execution. We conducted a literature survey to identify the challenging factors associated with QSE process and mapped them into 7 core categories. Additionally, we used a questionnaire survey to collect insights from practitioners regarding these challenges. To examine the relationships between core categories of challenging factors, we applied Interpretive Structure Modeling (ISM). Lastly, we applied fuzzy TOPSIS to rank the identified challenging factors concerning to their criticality for QSE process. We have identified 22 challenging factors of QSE process and mapped them to 7 core categories. The ISM results indicate that the ‘resources’ category has the most decisive influence on the other six core categories of the identified challenging factors. Moreover, the fuzzy TOPSIS indicates that ‘complex programming’, ‘limited software libraries’, ‘maintenance complexity’, ‘lack of training and workshops’, and ‘data encoding issues’ are the highest priority challenging factor for QSE process execution. Organizations using QSE could consider the identified challenging factors and their prioritization to improve their QSE process.
Context: Quantum software development is a complex and intricate process that diverges significantly from traditional software development. Quantum computing and quantum software are deeply entangled with quantum mechanics, which introduces a different level of abstraction and a deep dependence on quantum physical properties. The classical requirements engineering methods must be adapted to encompass the essential quantum features in this new paradigm. Aim: This study aims to systematically identify and analyze challenges, opportunities, developments, and new lines of research in requirements engineering for quantum computing. Method: We conducted a systematic literature review, including three research questions. This study included 105 papers published from 2017 to 2024. Results: The main results include the identification of problems associated with defining specific requirements for quantum software and hybrid system requirements. In addition, we identified challenges related to the absence of standards for quantum requirements engineering. Finally, we can see the advances in developing programming languages and simulation tools for developing software in hybrid systems. Conclusions: This study presents the challenges and opportunities in quantum computing requirements engineering, emphasizing the need for new methodologies and tools. It proposes a roadmap for future research to develop a standardized framework, contributing to theoretical foundations and practical applications.
Quantum computing, particularly in the area of quantum optimization, is steadily progressing toward practical applications, supported by an expanding range of hardware platforms and simulators. While Software Engineering (SE) optimization has a strong foundation, which is exemplified by the active Search-Based Software Engineering (SBSE) community and numerous classical optimization methods, the growing complexity of modern software systems and their engineering processes demands innovative solutions. This Systematic Literature Review (SLR) focuses specifically on studying the literature that applies quantum or quantum-inspired algorithms to solve classical SE optimization problems. We examine 76 primary studies selected from an initial pool of 2083 publications obtained through systematic searches of six digital databases using carefully crafted search strings. Our findings reveal concentrated research efforts in areas such as SE operations and software testing, while exposing significant gaps across other SE activities. Additionally, the SLR uncovers relevant works published outside traditional SE venues, underscoring the necessity of this comprehensive review. Overall, our study provides a broad overview of the research landscape, empowering the SBSE community to leverage quantum advancements in addressing next-generation SE challenges.
The quantum computing (QC) field is rapidly moving beyond the realm of pure science to become a commercially viable technology that may be able to overcome the drawbacks of traditional computing. Major technology tycoons have spent in building coding frameworks and hardware to create applications specifically designed for quantum computing over the last few years. The development of QC hardware is accelerating, however, the requirement for software-intensive methodology, approaches, procedures, instruments, roles and responsibilities for creating industrial-focused quantum software applications arises from operationalizing the QC. This paper outlines the concept of quantum software engineering (QSE) life cycle, which entails the engineering of quantum requirements, design, implementation, testing and maintenance of quantum software. This paper notably advocates for collaborative efforts between the industrial community and software engineering research to propose practical solutions to support the complete activities for the development of quantum software. The proposed vision makes it easier for researchers and practitioners to suggest new procedures, reference designs, cutting-edge equipment, and methods for utilizing quantum computers and creating the newest and most advanced quantum software.
The increasing maturity of Quantum Computing (QC) and quantum hardware hasbrought Quantum Software Design (QSD) into focus as a distinct research concernwithin Quantum Software Engineering (QSE), yet no prior study has systematicallymapped its approaches, challenges, and opportunities. This thesis addresses thatgap through two complementary research phases: a Systematic Literature Review(SLR) of 32 studies published between 2006 and 2025, and semi-structured inter-views with four domain experts. Both phases confirm that quantum-classical hybrid design is the dominant approach in current QSD research, and that the adaptationof classical tools, particularly Unified Modeling Language (UML) and Model-DrivenEngineering (MDE), is the primary methodological strategy. Hardware-software co-design is identified as a necessary design concern under Noisy Intermediate-Scale Quantum (NISQ) constraints; two further directions absent from the published liter-ature were identified by practitioners: AI-assisted QSD and self-adaptive quantumsystems. The challenge analysis reveals a potential causal sequence of challenges in the field. Human resources challenges, specifically, the bidirectional knowledge gap between software engineers and quantum computing researchers, emerge exclusively from the interviews and are absent from all 32 reviewed studies. MDE is the most convergent near-term opportunity, and the formalization of human resources and educational pathways represents a new research direction not addressed in the existing literature.
Quantum systems have started to emerge as a disruptive technology and enabling platforms - exploiting the principles of quantum mechanics - to achieve quantum supremacy in computing. Academic research, industrial projects (e.g., Amazon Braket), and consortiums like 'Quantum Flagship' are striving to develop practically capable and commercially viable quantum computing (QC) systems and technologies. Quantum Computing as a Service (QCaaS) is viewed as a solution attuned to the philosophy of service-orientation that can offer QC resources and platforms, as utility computing, to individuals and organisations who do not own quantum computers. To understand the quantum service development life cycle and pinpoint emerging trends, we used evidence-based software engineering approach to conduct a systematic mapping study (SMS) of research that enables or enhances QCaaS. The SMS process retrieved a total of 55 studies, and based on their qualitative assessment we selected 9 of them to investigate (i) the functional aspects, design models, patterns, programming languages, deployment platforms, and (ii) trends of emerging research on QCaaS. The results indicate three modelling notations and a catalogue of five design patterns to architect QCaaS, whereas Python (native code or frameworks) and Amazon Braket are the predominant solutions to implement and deploy QCaaS solutions. From the quantum software engineering (QSE) perspective, this SMS provides empirically grounded findings that could help derive processes, patterns, and reference architectures to engineer software services for QC.