This study develops a technology analysis to detect technological trajectories in quantum computing that can lay the foundation of a quantum industry. Study design considers data of 10,089 scientific products in ‘quantum computing’ over 1989–2020 period and 19,266 scientific products in ‘quantum computer’ over 1967–2020 period to cover all scientific production in this research field; a complementary analysis considers 8,505 patents over 1977–2020 in ‘quantum computing’ and 9,792 patents in ‘quantum computer’ over 1985–2020 period. Models of technological evolution applied here show main technological trajectories in quantum computing and computer and their rates of growth that suggest path-breaking directions in quantum technology, such as quantum optics, quantum information, quantum algorithms, quantum entanglement, quantum communication and quantum cryptography. Findings here provide information to extend the knowledge in the evolution of quantum computing and computers to support the management of these new technologies and R&D investments towards innovations generating industrial, economic and social change.
This study develops a technology analysis to detect technological trajectories in quantum computing that can lay the foundation of a quantum industry. Study design considers data of 10,089 scientific products in ‘quantum computing’ over 1989–2020 period and 19,266 scientific products in ‘quantum computer’ over 1967–2020 period to cover all scientific production in this research field; a complementary analysis considers 8,505 patents over 1977–2020 in ‘quantum computing’ and 9,792 patents in ‘quantum computer’ over 1985–2020 period. Models of technological evolution applied here show main technological trajectories in quantum computing and computer and their rates of growth that suggest path-breaking directions in quantum technology, such as quantum optics, quantum information, quantum algorithms, quantum entanglement, quantum communication and quantum cryptography. Findings here provide information to extend the knowledge in the evolution of quantum computing and computers to support the management of these new technologies and R&D investments towards innovations generating industrial, economic and social change.
Quantum computing is a vital research field in science and technology. One of the fundamental questions hardly known is how quantum computing research is developing to support scientific advances and the evolution of path-breaking technologies for economic, industrial, and social change. This study confronts the question here by applying methods of computational scientometrics for publication analyses to explain the structure and evolution of quantum computing research and technologies over a 30-year period. Results reveal that the evolution of quantum computing from 1990 to 2020 has a considerable average increase of connectivity in the network (growth of degree centrality measure), a moderate increase of the average influence of nodes on the flow between nodes (little growth of betweenness centrality measure), and a little reduction of the easiest access of each node to all other nodes (closeness centrality measure). This evolutionary dynamics is due to the increase in size and complexity of the network in quantum computing research over time. This study also suggests that the network of quantum computing has a transition from hardware to software research that supports accelerated evolution of technological pathways in quantum image processing, quantum machine learning, and quantum sensors. Theoretical implications of this study show the morphological evolution of the network in quantum computing from a symmetric to an asymmetric shape driven by new inter-related research fields and emerging technological trajectories. Findings here suggest best practices of innovation management based on R&D investments in new technological directions of quantum computing having a high potential for growth and impact in science and markets.
Abstract Quantum computer and computing are areas of theoretical and experimental research having a phase of growth that can generate a tectonic shift of the evolution of technology in society. The scientific and technological development in quantum research is due to a range of driving technological trajectories that are growing to solve more and more complex problems. The goal of this study is to detect emerging research fields and technological trajectories of quantum computing to explain and generalize, whenever possible, the technological characteristics of evolutionary dynamics in science and technology. Database of Scopus concerning documents and patents is used for statistical analyses to determine the growth of research fields and technological trajectories having a high potential impact in science and society. Results suggest that quantum research is driven by emerging scientific and technological trajectories given by Qubits, Quantum optics, Quantum circuit, Semiconductor quantum dots and quantum information. Overall, this study explains, whenever possible, emerging research fields and technological technologies in quantum computing that support scientific and technological change directed to future economic and social progress. Finally, technology analysis of this study can help policymakers to support the allocation of resources for all areas of Quantum Science having a high potential of growth and positive impact in science and society.
Abstract - Quantum computing has evolved from a physics-driven theoretical concept into a rapidly expanding technological ecosystem encompassing computation, communication, sensing, and security. This paper presents a timeline-based analytical study of this evolution, examining how successive scientific and technological eras shaped the transition from foundational quantum theory to modern noisy intermediate-scale quantum (NISQ) platforms and distributed quantum infrastructures. Rather than offering a purely chronological account, the study evaluates each phase using comparative metrics such as scientific maturity, technological capability, scalability, error resilience, application readiness, and interdisciplinary integration. Beyond technical progress, the paper highlights the growing role of academic institutions in developing foundational methods and workforce capacity, alongside industrial efforts in hardware engineering, cloud-based access, and commercialization. These developments are further reinforced by large-scale public investments through national quantum missions and regional innovation programs, positioning quantum technologies as strategic components of future digital infrastructure. By integrating historical evolution with ecosystem and policy perspectives, this work aims to clarify how scientific constraints, engineering challenges, and institutional drivers collectively shape the current trajectory and future direction of quantum technologies. Key Words: quantum, timeline, global impact, hybrid, evolution
Quantum technology has garnered considerable attention for its transformative potential across various industries since the early 2000s, precipitating a surge in quantum start-ups and substantial investments worldwide. However, comprehensive research on quantum technology in industries, particularly start-ups in the quantum field, remains limited. This study analyzes a dataset of 1,200 quantum start-ups from 40 countries, specifically focusing on 430 notable active start-ups. The analysis provides a thorough perspective on the industry’s progress, including trends in growth, geographical distribution, industry categorization, and investment patterns. The study categorizes start-ups into five domains: quantum computing, communication, sensing, hardware, and software. The analysis further captures the progress made by ten prominent quantum start-ups, six major technology companies, and quantum consortia regarding their technological innovations and strategic partnerships. We provide a comparative analysis of prior research to identify current conditions and future implications validated by notable studies, focusing on key parameters such as dataset scope, geographical distribution, growth trends, industry categorization, taxonomy, main interests in quantum fields, key players, and ecosystem identification. This approach underscores the dynamic growth of the quantum industry, which will drive future advancements.
The rapid growth of computational technology has given rise to new paradigms that aim to redefine the limits of traditional computing. Of these, quantum computing is a paradigm change that applies to the principles of quantum mechanics, such as superposition and entanglement, to compute at much higher rates than classical computing is capable of. This paper covers the evolution of computing paradigms, focusing on quantum computing. It examines critically the principles, paradigm of programming and performance elements that differentiate quantum computing from mainstream computing. It also investigates scalability challenges and technologies that are advancing quantum computing technology. The work further investigates disruptive applications of quantum computing in key industries like artificial intelligence, cryptography, medicine modeling, and financial modeling. Despite current constraints in terms of hardware limitations and error correction, the potential for quantum computing is vast in redesigning problem-solving approaches across industries. This consideration emphasizes the need for continued research and development to tap into the full potential of quantum computing in solving complex computational problems.
Quantum computing (QC) has the potential to be the next abstruse technology, with a wide range of possible applications and ramifications for organizations and markets. QC provides an exponential speedup by employing quantum mechanics principles, including superposition and entanglement. The potential advantages offered by the revolutionary paradigm have propelled scientific productions. Therefore, a highly pertinent investigation is required to elucidate the evolution of path-breaking trajectories for scientific advances. This study confronts the idea by presenting a scientometric analysis for the recent decade of literature collected from the Web of Science database in the computer science discipline. The scientometric implications of the article identify the significant research domains and provide an intensive insight into the publication patterns, country collaboration, geographical analysis, citation patterns, eminent journals, and research frontiers of each domain of QC. The scholarly literature analysis identifies key challenges in the QC knowledge domain. Overall, the inference reveals an evolutionary pathway for future research directives and collaboration in the domains of QC research. The research findings provide innovative significance to information scientists by presenting a comprehensive overview of QC research to help them find relevant applications, research topics, and key challenges.