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.
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.
Quantum computing systems rely on the principles of quantum mechanics to perform a multitude of computationally challenging tasks more efficiently than their classical counterparts. The architecture of software-intensive systems can empower architects who can leverage architecture-centric processes, practices, description languages to model, develop, and evolve quantum computing software (quantum software for short) at higher abstraction levels. We conducted a Systematic Literature Review (SLR) to investigate (i) architectural process, (ii) modelling notations, (iii) architecture design patterns, (iv) tool support, and (iv) challenging factors for quantum software architecture. Results of the SLR indicate that quantum software represents a new genre of software-intensive systems; however, existing processes and notations can be tailored to derive the architecting activities and develop modelling languages for quantum software. Quantum bits (Qubits) mapped to Quantum gates (Qugates) can be represented as architectural components and connectors that implement quantum software. Tool-chains can incorporate reusable knowledge and human roles (e.g., quantum domain engineers, quantum code developers) to automate and customise the architectural process. Results of this SLR can facilitate researchers and practitioners to develop new hypotheses to be tested, derive reference architectures, and leverage architecture-centric principles and practices to engineer emerging and next generations of quantum software.
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.
This chapter discusses a systematic review of the literature available on quantum computers from their theoretical bases through their current advances to their trends in the future. It uses the PRISMA approach to collect such writings from many fields, including physics, computer science, and engineering. It identifies crucial breakthroughs, including Feynman’s conjecture and Shor’s algorithm, and examines how they have affected modern quantum computing paradigms. The said focus areas will center on quantum algorithms, hardware developments in quantum information technology, cryptography, and machine learning, all with an emphasis on their combined contribution to the growth of the field. Identified milestones include achievements of the so-called quantum supremacy and advances made possible by qubit technologies, such as superconducting circuits and ion traps. The review not only presents future challenges, such as scalability, error correction, and noise management, but also pinpoints research gaps and possible avenues for subsequent exploration. The chapter examines the themes of interdisciplinary collaboration and funding trends as well as emerging issues such as quantum cloud computing and hybrid quantum-classical models. Such an overview of the trajectory of quantum computing by this review is also an academic reference as well as a strategic guide for researchers, policymakers, and technologists traversing this fast-evolving field.
Quantum computing systems rely on the principles of quantum mechanics to perform a multitude of computationally challenging tasks more efficiently than their classical counterparts. The architecture of software-intensive systems can empower architects who can leverage architecture-centric processes, practices, description languages, etc., to model, develop, and evolve quantum computing software (quantum software for short) at higher abstraction levels. We conducted a systematic literature review (SLR) to investigate (i) architectural process, (ii) modeling notations, (iii) architecture design patterns, (iv) tool support, and (iv) challenging factors for quantum software architecture. Results of the SLR indicate that quantum software represents a new genre of software-intensive systems; however, existing processes and notations can be tailored to derive the architecting activities and develop modeling languages for quantum software. Quantum bits (Qubits) mapped to Quantum gates (Qugates) can be represented as architectural components and connectors that implement quantum software. Tool-chains can incorporate reusable knowledge and human roles (e.g., quantum domain engineers, quantum code developers) to automate and customize the architectural process. Results of this SLR can facilitate researchers and practitioners to develop new hypotheses to be tested, derive reference architectures, and leverage architecture-centric principles and practices to engineer emerging and next generations of quantum software.
This paper presents a comprehensive systematic and bibliometric review of the rapidly expanding literature on quantum computing. The study addresses six research questions concerning prominent themes, temporal evolution, types of key findings, frequently reported research gaps, higher-order gap typologies, and future research directions. Utilizing a researcher-curated database, the final corpus comprises 153 studies compiled from non-Scopus, Scopus and Web of Science. The results demonstrate that quantum hardware and architecture, algorithms, and theoretical foundations dominate the current literature. Furthermore, publication output has risen significantly between 2018 and 2025. Among the reported findings, algorithmic innovations, performance claims, and hardware advancements are the most common. Despite this progress, the field faces substantial barriers; technical, scalability, fault-tolerance, and methodological-standardization gaps are the most frequently reported. Based on these identified gaps, eight actionable future research directions emerge to guide the scientific community. Ultimately, this review concludes that quantum computing is actively transitioning from a purely physics-driven domain into a multidisciplinary, engineering- and application-oriented science. However, its continued advancement is presently constrained by a hardware-methodology-application triad, wherein reporting practices often outpace empirical verification and standardized benchmarking.
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.