Abstract Quantum computing (QC) advances will help solve high complexity challenges that classical system architectures cannot solve. This report examines the philosophical foundation for QC research using a PRISMA-compliant systematic literature review and bibliometric analysis. This review emphasizes business applications of QC, including optimization, cryptography and cybersecurity, machine learning, financial modeling and forecasting, supply chain management, healthcare and analytics, and materials design, showing how QC can improve prediction and forecast accuracy, data physicalness, large-scale optimization, and more. The research will also identify execution constraints such hardware readiness, qubit capacity, development and operation expenses, personnel availability, and regulatory and commercial governance. Bibliometric data showed a focus on foundational quantum theoretical contributions and algorithms, with multidisciplinary and practical work emerging. Scaled algorithms, quantum-resistant standards, and any solutions industry nexus to enterprise are among the research opportunities. The literature evaluation will also identify ethical, policy, and sustainability issues related to quantum benefits. The paper helps businesspeople understand QC’s current and future opportunities, including a research-based assessment of where we’ve come, common themes in the literature, and strategic domains for scaling QC as a research agenda from laboratory experiments to enriching the service experience as commercial utility.
Abstract Quantum computing (QC) advances will help solve high complexity challenges that classical system architectures cannot solve. This report examines the philosophical foundation for QC research using a PRISMA-compliant systematic literature review and bibliometric analysis. This review emphasizes business applications of QC, including optimization, cryptography and cybersecurity, machine learning, financial modeling and forecasting, supply chain management, healthcare and analytics, and materials design, showing how QC can improve prediction and forecast accuracy, data physicalness, large-scale optimization, and more. The research will also identify execution constraints such hardware readiness, qubit capacity, development and operation expenses, personnel availability, and regulatory and commercial governance. Bibliometric data showed a focus on foundational quantum theoretical contributions and algorithms, with multidisciplinary and practical work emerging. Scaled algorithms, quantum-resistant standards, and any solutions industry nexus to enterprise are among the research opportunities. The literature evaluation will also identify ethical, policy, and sustainability issues related to quantum benefits. The paper helps businesspeople understand QC’s current and future opportunities, including a research-based assessment of where we’ve come, common themes in the literature, and strategic domains for scaling QC as a research agenda from laboratory experiments to enriching the service experience as commercial utility.
The emerging quantum computing technology (QC) transforms business intelligence by creating new methods for marketing and finance to use data in their decision-making processes. This research combines a systematic literature review with a meta-analysis of 26 peer-reviewed articles spanning from 2014 to 2025 to show how quantum computing improves predictive analytics and customer segmentation and sentiment analysis, portfolio optimization, and risk modeling. The research uses PRISMA guidelines together with strategic foresight tools to detect how hybrid quantum-classical systems replace classical models by delivering exponential speed and accuracy, and scalability benefits. The research demonstrates that QSVM, QAOA, and quantum-enhanced neural networks serve as primary methodologies to create real-time, emotionally intelligent, highly adaptive decision systems. The thematic analysis, together with conceptual mapping, shows that quantum tools are converging into a unified interdisciplinary space that combines marketing with finance and supply chains and pricing strategies. The study reveals important research deficiencies in multilingual datasets as well as benchmarking and ethical readiness, and suggests a strategic integration plan for global implementation by 2030. The research demonstrates that quantum computing represents a strategic capability that enables future-ready data-driven enterprises to enter a new business intelligence era based on quantum resilience.
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 (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.
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.
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.
Quantum Computing is gradually gaining importance as a transformative technology with the potential to revolutionize business applications by solving complicated problems beyond the capabilities of classical computers. Using basic principles such as superposition and entanglement, quantum systems can concurrently handle a wide range of combinations of possibilities. It offers several benefits in optimization, financial modeling, supply chain management, risk analysis, and machine learning. Despite the limitations of existing hardware in the NISQ era, rapid developments are enhancing scalability and reliability. This study analyses the practical implications, opportunities, and challenges of integrating quantum computing into business environments, spotlighting its capacity to drive innovation, competitive advantage, and long-term strategic growth throughout industries. By situating quantum computing within a transdisciplinary engineering and business ecosystem, this review demonstrates that quantum technologies are not merely enablers of computational speed but catalysts for systemic innovation.