IDEAS home Printed from https://ideas.repec.org/a/spr/jknowl/v16y2025i1d10.1007_s13132-024-01977-y.html
   My bibliography  Save this article

Path-Breaking Directions in Quantum Computing Technology: A Patent Analysis with Multiple Techniques

Author

Listed:
  • Mario Coccia

    (CNR - National Research Council of Italy, IRCRES - Turin Research Area of the National Research Council, Strada Delle Cacce)

  • Saeed Roshani

    (Amirkabir University of Technology)

Abstract

The rapid advancement of quantum computing technology has profound implications in knowledge economy for various sectors including cybersecurity, healthcare, finance, and logistics, among others. The understanding of evolutionary patterns in quantum computing is a basic goal for strategic planning and technological development of nations. This study applies, using patent data, different approaches, such as the logistic model and the entity-linking technique, to analyze the evolutionary trajectories of topics in quantum computing. Technology analysis of patents here detects three distinctive stages—the emerging stage (1992–2008), the growth stage (2009–2017), and the maturity stage (2018–2022)— and shows main characteristics of the technology life cycle in quantum computing for technological forecasting and management. Logistic model suggests that quantum computing technology seems to be in a maturity stage, as evidenced by a surge in patent filings since 2016. Dominant topics are given by qubits, quantum gates, quantum information, and quantum dots exhibit exponential growth, and suggest their pivotal role in technological evolution of quantum computing. In addition, the entity-linking method uncovers complex and evolving interconnections in quantum computing topics over time: a suggested categorization in emerging, declining, dominant, and saturated topics clarifies critical groups that guide new directions of technological progress in quantum computing. The insights of this study can shed light on complex scientific and technological dynamics that drive the co-evolution of quantum computing technologies that can support strategies of innovation management and policies to foster technological change for competitive advantage of firms and nations in turbulent markets.

Suggested Citation

  • Mario Coccia & Saeed Roshani, 2025. "Path-Breaking Directions in Quantum Computing Technology: A Patent Analysis with Multiple Techniques," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 4991-5024, March.
  • Handle: RePEc:spr:jknowl:v:16:y:2025:i:1:d:10.1007_s13132-024-01977-y
    DOI: 10.1007/s13132-024-01977-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13132-024-01977-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13132-024-01977-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Donghyun Choi & Bomi Song, 2018. "Exploring Technological Trends in Logistics: Topic Modeling-Based Patent Analysis," Sustainability, MDPI, vol. 10(8), pages 1-26, August.
    2. Magee, C.L. & Basnet, S. & Funk, J.L. & Benson, C.L., 2016. "Quantitative empirical trends in technical performance," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 237-246.
    3. Sinigaglia, Tiago & Eduardo Santos Martins, Mario & Cezar Mairesse Siluk, Julio, 2022. "Technological evolution of internal combustion engine vehicle: A patent data analysis," Applied Energy, Elsevier, vol. 306(PA).
    4. Mario COCCIA, 2017. "The Fishbone diagram to identify, systematize and analyze the sources of general purpose technologies," Journal of Social and Administrative Sciences, KSP Journals, vol. 4(4), pages 291-303, December.
    5. Mario Coccia, 2020. "The evolution of scientific disciplines in applied sciences: dynamics and empirical properties of experimental physics," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 451-487, July.
    6. Lin, Deming & Liu, Wenbin & Guo, Yinxin & Meyer, Martin, 2021. "Using technological entropy to identify technology life cycle," Journal of Informetrics, Elsevier, vol. 15(2).
    7. Chun-Chieh Wang & Hui-Yun Sung & Mu-Hsuan Huang, 2016. "Technological evolution seen from the USPC reclassifications," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 537-553, May.
    8. Yuan, Xiaodong & Li, Xiaotao, 2020. "A network analytic method for measuring patent thickets: A case of FCEV technology," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    9. Huang, Ying & Li, Ruinan & Zou, Fang & Jiang, Lidan & Porter, Alan L. & Zhang, Lin, 2022. "Technology life cycle analysis: From the dynamic perspective of patent citation networks," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    10. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    11. Mario Coccia, 2018. "General properties of the evolution of research fields: a scientometric study of human microbiome, evolutionary robotics and astrobiology," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 1265-1283, November.
    12. Aharonson, Barak S. & Schilling, Melissa A., 2016. "Mapping the technological landscape: Measuring technology distance, technological footprints, and technology evolution," Research Policy, Elsevier, vol. 45(1), pages 81-96.
    13. Huailan Liu & Zhiwang Chen & Jie Tang & Yuan Zhou & Sheng Liu, 2020. "Mapping the technology evolution path: a novel model for dynamic topic detection and tracking," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2043-2090, December.
    14. Mauricio Marrone, 2020. "Application of entity linking to identify research fronts and trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 357-379, January.
    15. Mauricio Marrone & Sascha Lemke & Lutz M. Kolbe, 2022. "Entity linking systems for literature reviews," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3857-3878, July.
    16. Kevin W. Boyack & Katy Börner & Richard Klavans, 2009. "Mapping the structure and evolution of chemistry research," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(1), pages 45-60, April.
    17. Chen, Baitong & Tsutsui, Satoshi & Ding, Ying & Ma, Feicheng, 2017. "Understanding the topic evolution in a scientific domain: An exploratory study for the field of information retrieval," Journal of Informetrics, Elsevier, vol. 11(4), pages 1175-1189.
    18. Saeed Roshani & Mohammad-Reza Bagherylooieh & Melika Mosleh & Mario Coccia, 2021. "What is the relationship between research funding and citation-based performance? A comparative analysis between critical disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7859-7874, September.
    19. Coccia, Mario, 2022. "Probability of discoveries between research fields to explain scientific and technological change," Technology in Society, Elsevier, vol. 68(C).
    20. Melika Mosleh & Saeed Roshani & Mario Coccia, 2022. "Scientific laws of research funding to support citations and diffusion of knowledge in life science," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1931-1951, April.
    21. Hou, Hong & Shi, Yongjiang, 2021. "Ecosystem-as-structure and ecosystem-as-coevolution: A constructive examination," Technovation, Elsevier, vol. 100(C).
    22. Chu, Wen-Lin & Wu, Feng-Shang & Kao, Kai-Sheng & Yen, David C., 2009. "Diffusion of mobile telephony: An empirical study in Taiwan," Telecommunications Policy, Elsevier, vol. 33(9), pages 506-520, October.
    23. Mario COCCIA, 2017. "Disruptive firms and industrial change," Journal of Economic and Social Thought, KSP Journals, vol. 4(4), pages 437-450, December.
    24. Mario Coccia, 2017. "Sources of disruptive technologies for industrial change," L'industria, Società editrice il Mulino, issue 1, pages 97-120.
    25. Hakyeon Lee & Pilsung Kang, 2018. "Identifying core topics in technology and innovation management studies: a topic model approach," The Journal of Technology Transfer, Springer, vol. 43(5), pages 1291-1317, October.
    26. Mario Coccia, 2008. "New organisational behaviour of public research institutions: lessons learned from Italian case study," International Journal of Business Innovation and Research, Inderscience Enterprises Ltd, vol. 2(4), pages 402-419.
    27. Coccia, Mario, 2023. "New Perspectives in Innovation Failure Analysis: A taxonomy of general errors and strategic management for reducing risks," Technology in Society, Elsevier, vol. 75(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mario Coccia & Saeed Roshani, 2024. "Evolution of topics and trends in emerging research fields: multiple analyses with entity linking, Mann–Kendall test and burst methods in cloud computing," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5347-5371, September.
    2. Higashide, Noriyuki & Zhang, Yi & Asatani, Kimitaka & Miura, Takahiro & Sakata, Ichiro, 2024. "Quantifying advances from basic research to applied research in material science," Technovation, Elsevier, vol. 135(C).
    3. Coccia, Mario, 2022. "Probability of discoveries between research fields to explain scientific and technological change," Technology in Society, Elsevier, vol. 68(C).
    4. Mario Coccia, 2021. "Evolution and structure of research fields driven by crises and environmental threats: the COVID-19 research," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9405-9429, December.
    5. Melika Mosleh & Saeed Roshani & Mario Coccia, 2022. "Scientific laws of research funding to support citations and diffusion of knowledge in life science," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1931-1951, April.
    6. Ruifeng Hu & Weiqiao Xu & Yalin Yang & Guangxian Ni, 2024. "A Combined Scientometric and Meta-analysis Exploration of Eco-innovation: Evolution and Determinants," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 3174-3201, March.
    7. Sajjad Shokouhyar & Mehrdad Maghsoudi & Shahrzad Khanizadeh & Saeid Jorfi, 2024. "Analyzing supply chain technology trends through network analysis and clustering techniques: a patent-based study," Annals of Operations Research, Springer, vol. 341(1), pages 313-348, October.
    8. Luz Judith R. Esparza & Ángel Lee & Carmen Rubio, 2024. "Influence of cultural and socioeconomic factors on scientific production: a statistical analysis of the h-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(4), pages 2083-2099, April.
    9. Mario COCCIA, 2018. "Types of government and innovative performance of countries," Journal of Social and Administrative Sciences, KSP Journals, vol. 5(1), pages 15-33, March.
    10. Harrison, Richard T., 2023. "W(h)ither entrepreneurship? Discipline, legitimacy and super-wicked problems on the road to nowhere," Journal of Business Venturing Insights, Elsevier, vol. 19(C).
    11. Yuanrong Zhang & Wei Guo & Jian Ma & Zhonglin Fu & Zhixing Chang & Lei Wang, 2023. "Evolution analysis of cross-domain collaborative research topic: a case study of cognitive-based product conceptual design," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(12), pages 6695-6718, December.
    12. Mario COCCIA, 2018. "Evolution of the economics of science in the Twenty Century," Journal of Economics Library, KSP Journals, vol. 5(1), pages 65-84, March.
    13. Mario COCCIA, 2018. "Theorem of not independence of any technological innovation," Journal of Economics Bibliography, KSP Journals, vol. 5(1), pages 29-35, March.
    14. Mazaheri, Maryam & Bonnin Roca, Jaime & Markus, Arjan & Tur, Elena M. & Walrave, Bob, 2024. "Maturity assessment of green patent clusters: Methodological implications," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    15. Garima Toor & Neha Goyal Tater & Tarush Chandra, 2024. "Exploring recent trends in integrating urban planning and ecology," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(8), pages 19093-19111, August.
    16. François Lafond & Daniel Kim, 2019. "Long-run dynamics of the U.S. patent classification system," Journal of Evolutionary Economics, Springer, vol. 29(2), pages 631-664, April.
    17. Mario Coccia, 2018. "Measurement of the evolution of technology: A new perspective," Papers 1803.08698, arXiv.org.
    18. Huang, Ying & Li, Ruinan & Zou, Fang & Jiang, Lidan & Porter, Alan L. & Zhang, Lin, 2022. "Technology life cycle analysis: From the dynamic perspective of patent citation networks," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    19. Podrecca, Matteo & Culot, Giovanna & Tavassoli, Sam & Orzes, Guido, 2024. "Artificial intelligence for climate change: a patent analysis in the manufacturing sector," Papers in Innovation Studies 2024/12, Lund University, CIRCLE - Centre for Innovation Research.
    20. Mario Coccia, 2020. "The evolution of scientific disciplines in applied sciences: dynamics and empirical properties of experimental physics," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 451-487, July.

    More about this item

    Keywords

    Quantum computing; Patent analysis; Topic modeling; Entity linking; S-Curve analysis; Logistic model; Technometrics; Technological change; Innovation management; Knowledge economy;
    All these keywords.

    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jknowl:v:16:y:2025:i:1:d:10.1007_s13132-024-01977-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.