Context: Quantum computing (QC) represents a paradigm shift in computational capabilities, presenting unique challenges in requirements engineering (RE). The complexity of quantum systems and rapid technological advancements necessitate a comprehensive understanding of the current state and future trajectories in RE for QC. Objective: A protocol for carrying out a systematic literature review about the evidence for identifying and analyzing the challenges in RE for QC software. It seeks to evaluate the current methodologies employed in this domain and propose a forward-looking perspective on the evolution of these methodologies to meet future industry and academic needs. Method: This protocol employs a structured approach to search and analyze relevant literature systematically, according to Barbara Kitchenham's guidelines. Results: A validated protocol to conduct a systematic review. The protocol is expected to yield diverse literature spanning theoretical frameworks, empirical studies, and methodological advancements in RE for QC. It will highlight the current challenges, opportunities, and future directions, offering insights into the field's academic and practical aspects. Conclusions: The systematic review aims to provide a nuanced understanding of the RE landscape in QC. It will offer valuable insights for academic researchers, industry professionals, software engineers, industry analysts, and educators, shaping the future discourse in QC development.
Context: Quantum computing (QC) represents a paradigm shift in computational capabilities, presenting unique challenges in requirements engineering (RE). The complexity of quantum systems and rapid technological advancements necessitate a comprehensive understanding of the current state and future trajectories in RE for QC. Objective: A protocol for carrying out a systematic literature review about the evidence for identifying and analyzing the challenges in RE for QC software. It seeks to evaluate the current methodologies employed in this domain and propose a forward-looking perspective on the evolution of these methodologies to meet future industry and academic needs. Method: This protocol employs a structured approach to search and analyze relevant literature systematically, according to Barbara Kitchenham's guidelines. Results: A validated protocol to conduct a systematic review. The protocol is expected to yield diverse literature spanning theoretical frameworks, empirical studies, and methodological advancements in RE for QC. It will highlight the current challenges, opportunities, and future directions, offering insights into the field's academic and practical aspects. Conclusions: The systematic review aims to provide a nuanced understanding of the RE landscape in QC. It will offer valuable insights for academic researchers, industry professionals, software engineers, industry analysts, and educators, shaping the future discourse in QC development.
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 represents one of the most transformative technological shifts in computational science since the advent of classical computing architectures. This systematic literature review (SLR) synthesizes 212 peer-reviewed studies published between 2015 and 2024 to map the current landscape of quantum computing research as it intersects with software development practices, tools, frameworks, and methodologies. Following the PRISMA 2020 guidelines, we searched six major academic databases and identified relevant literature through a rigorous multi-stage screening process. Our analysis reveals five dominant research themes: quantum algorithm development and software frameworks, quantum machine learning integration, quantum cryptography and security applications, quantum software testing and verification, and hybrid quantum-classical software architectures. We further document major challenges including hardware decoherence, limited qubit availability, the absence of mature quantum DevOps pipelines, and a critical shortage of quantum-competent software engineers. The review concludes with a structured research agenda identifying six high-priority gaps where future scholarly work is most urgently needed. This paper provides a foundational reference for software engineers, researchers, and technology strategists seeking to understand and navigate the rapidly evolving quantum software ecosystem.
Abstract Quantum computing (QC) hinged upon the bedrock principles of quantum theory and holds promise for reforming a large number of industries. The researcher in this area aims to deliver a comprehensive understanding of the current state of the art and future trajectories of QC. The authors have discovered that most academic studies have concentrated upon dissecting specific aspects of QC. This discernment underscores the exigency of identifying challenges that might impede the seamless integration of QC within the software industry. Moreover, it becomes crucial to ascertain the panoply of solutions/practices required to overcome these barriers. A comprehensive multi‐vocal literature review was performed and culled a total of 49 academic papers for data extraction. A total of 13 challenges encountered by organisations were identified during the adoption of QC. Subsequently, these challenges were examined deeply and determined that five of them are the most critical, these are ‘Lack of quantum specific algorithms, dev and testing methodologies’, ‘Difficult compilation and debugging’, ‘Lack of development tools and technology’, ‘Lack of development guidelines & Quality Assurance Standards’ and ‘Lack of professional expert’, together founding over 30% of occurrences. These challenges from various perspectives were evaluated, including time frame, methodology, geographical region and publication platform. To address these barriers and implement the QC in software industry effectively, a total of 53 practices/solutions. This research aims to share valuable knowledge to simplify and amplify quantum application development.
Quantum computing is commonly described as a type of computation that uses the principles of quantum mechanics to process information. Specifically, quantum computers can handle complexity efficiently using superposition and enhanced problem-solving capabilities as a result of its utilization of the quantum bit instead of the bit used in classical computing. In the age of digital manipulation, the capabilities of quantum computers have allowed for a more holistic understanding of quantum computer’s ability to bring about solutions to cybersecurity, banking and finance, and advanced manufacturing systems. Some of the most prevalent challenges to develop quantum algorithms include benchmarking and error mitigation techniques necessary for software development and hardware implementation and interfacing. To address these shortcoming, Model-Based Systems Engineering (MBSE) is being proposed to develop a framework through a formalized application of modeling to support system requirements and traceability and create models to support system behavior and how quantum machines will interact with the software and hardware to increase stakeholder understanding. Other MBSE techniques like recursive validation and verification throughout the project lifecycle ensure that quantum-based projects reach the highest level of development. This research will use a systematic literature review methodology for evidence-informed research through a bibliometric analysis. Therefore, the aim of this literature review is to identify past or current trends in existing research that applies MBSE methods to quantum computing. This paper will identify where MBSE has been used to add value in quantum computing and set priorities for future work
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.
Quantum Computing (QC) and Artificial Intelligence (AI) have emerged as key technologies in the evolution of Industry 6.0, driving advancements in automation and advanced analytics, and process optimization. Their integration holds the potential to revolutionize sectors such as data science, healthcare, finance, and cybersecurity by enabling faster and more efficient computations through qubits, superposition, and quantum entanglement. However, the lack of structured knowledge regarding specific QC methodologies and applications in AI hinders its optimal implementation and development. Consequently, this study aims to identify the applications and variables associated with QC-AI integration. To this end, a systematic literature review was conducted following the PRISMA 2020 methodology, drawing on studies from Scopus and Web of Science databases. This enabled the analysis of trends, limitations, and opportunities in this technological convergence. This study aims to systematically examine the intersection of quantum computing and artificial intelligence by identifying the key technological features, integration requirements, and sectoral applications that define the current state of the field. The review contributes by mapping existing research, highlighting methodological approaches, and revealing gaps that may guide targeted advancements in hybrid quantum AI systems. The insights generated have the potential to accelerate innovation in high-impact domains such as healthcare, finance, energy, and cybersecurity. The findings indicate that the main advances in QC applied to AI focus on quantum optimization, Quantum Machine Learning (QML), and post-quantum cryptography. Notably, sectors such as energy, healthcare, and finance have shown significant progress in adopting these technologies. For example, in healthcare, QML has been applied to simulate molecular interactions to accelerate drug discovery, and in finance, it enhances predictive models for market behavior. The study concludes that although QC demonstrates substantial potential to enhance AI, its broader adoption remains constrained by reliance on NISQ hardware, the need for effective error correction, and the limited scalability of hybrid quantum classical algorithms. Addressing these challenges will be essential to establishing QML as a cornerstone of technological innovation and digital transformation. Additionally, this review introduces an integrative framework that categorizes key AI QC convergence dimensions and proposes a classification of application areas based on technical requirements and algorithmic capabilities. These contributions aim to guide future experimental validations and hybrid model development.