Research ArticleJournal of Scientometric ResearchVol. 15 | Issue 2 | 2026 | pp. 317–330Open access
How Scientific and Technological Interaction Accelerates Co-Evolution of Artificial Intelligence and Quantum Systems
- 1*
- 1 Department of Social Sciences, CNR-IRCRES, National Research Council of Italy, Strada Delle Cacce, Torino, ITALY.
Published in Journal of Scientometric Research
Correspondence: Mario Coccia
Department of Social Sciences, CNR-IRCRES, National Research Council of Italy, Strada Delle Cacce, Torino, ITALY.
Email: mario.coccia@cnr.it
Copyright: © 2026 Manuscript Technomedia. This is an open access article.
- Published:
- Aug 13, 2026
- Received:
- Mar 23, 2026
- Accepted:
- May 8, 2026
How to cite
Coccia, M. (2026). How Scientific and Technological Interaction Accelerates Co-Evolution of Artificial Intelligence and Quantum Systems. Journal of Scientometric Research, 15(2), 317–330. https://doi.org/10.5530/jscires.20260011
Abstract
This study investigates the drivers of recent scientific and technological advancements by examining the interaction between quantum and Artificial Intelligence (AI) systems. It analyzes how the scientific and technological coupling of these fields affects the pace of co-evolutionary dynamics. The research employs statistical models of evolutionary growth to analyze publication and patent trends in quantum and AI systems. Comparative growth rates are calculated to analyze synergetic effects between these domains. The results reveal that the coupling between AI and quantum systems exhibits a higher relative growth rate (1.58) compared to each scientific field individually (Quantum: 1.07; AI: 1.37). Statistical evidence indicates that AI, as a general-purpose technology, significantly drives progress in the combined domain. The interaction of these breakthrough fields fosters a mutually reinforcing cycle of scientific and technological advancement, accelerating co-evolutionary pathways. The analysis is based on publication and patent data, which may not fully capture informal knowledge flows or emerging research that has not yet been published. Furthermore, the study focuses on two domains only, limiting generalizability to other scientific and technological interactions. The findings suggest that science policy and R&D strategies should promote cross-domain collaboration and targeted investments directed to interactions in emerging fields. Encouraging integration between AI and quantum systems can amplify both science advances and innovation trajectories. This study provides a novel evidence-based explanation of how interaction-specifically between AI and quantum systems-is a driving force that accelerates scientific and technological evolution. Hence, this study contributes to the theoretical understanding of drivers in scientific and technological change in order to offer new strategies for innovation and science policies.
Keywords
Subject
Article metadata
| Title | How Scientific and Technological Interaction Accelerates Co-Evolution of Artificial Intelligence and Quantum Systems |
|---|---|
| Authors | Mario Coccia |
| Affiliations | Department of Social Sciences, CNR-IRCRES, National Research Council of Italy, Strada Delle Cacce, Torino, ITALY. |
| Corresponding author | mario.coccia@cnr.it |
| Journal | Journal of Scientometric Research |
| Volume / Issue | Vol. 15, Issue 2 (2026) |
Also in this issue
- Knowledge Mapping of Scientific Production on Microplastics and Nanoplastics in Food and Beverages: Bibliometric Evidence from a Decade (2015-2025)pp. 1–18
- A Bibliometric Analysis of Explainable Artificial Intelligence (XAI): Trends, Themes, and Global Research Dynamicspp. 331–346
- From Equations to Algorithms: A Bibliometric Analysis and Visualization of Physics-Informed Machine Learning Researchpp. 347–355
- ArguKPE: Argument-Driven Keyphrase Extraction with Transformer-Based Semantic Alignment for Reviewer-Manuscript Matchingpp. 356–364
- EEG-Based Machine Learning in Pediatric Epilepsy Research: Worldwide Patterns, Recent Trends and Future Prospects: A Bibliometric Studypp. 365–373
Readers Also Viewed
Development and Validation of UV/visible Spectrophotometric Method for Estimation of Piroxicam from Bulk and Formulation
Sandip Mohan Honmane, Kunal Rajaram Yadav, Yuvraj Dilip Dange
Apr 23, 2025
Effects of Artificial Intelligence on Academic Performance of Library and Information Science University Students: A Meta-Analysis (2023-2025)
Kayode Sunday John Dada
Aug 6, 2026
Bridging Innovation and Impact: A Multidisciplinary Approach to Contemporary Research Challenges
Mueen Ahmed KK
Aug 11, 2026