Forecasting Conceptual Diffusion in Science: The Case of Quantum Computing

Thomas Maillart; Thibaut Chataing; David Dosu; Paul Bagourd; Julian Jang-Jaccard; Alain Mermoud

paper

Forecasting Conceptual Diffusion in Science: The Case of Quantum Computing

arXiv:2606.03919

Abstract

Understanding and anticipating scientific change requires models that distinguish between endogenous consolidation and exogenous diffusion of scientific concepts. Using the quantum computing subtree of concepts in OpenAlex, we construct a temporally resolved concept co-occurrence network and track each concept pair through its upstream citation lineage and downstream diffusion. We train LightGBM models on distributional and diversity-aware features to predict four outcomes: endogenous reinforcement, exogenous diffusion, their ratio, and diffusion entropy. After controlling for overall publication growth of the scientific body, endogenous reinforcement proves largely unpredictable in the primary quantum-computing benchmark. In contrast, exogenous diffusion and entropy are strongly predictable ( $R^{2}$ up to $0.78 A ~) an d a r e d r i v e nb y u p s t r e amh e t er o g e n e i t y, c i t a t i o nb r e a d t h, an dd i s t r ib u t i o na l d i s p er s i o n, a ss h o w nb y S H A P ana l y ses; r e pl i c a t i o n so n r o b o t i cs, a d v an ce d ma t er ia l s, an d n e u r o im pl an t sco n f i r m t ha t e x o g e n o u s d i f f u s i o n r e main s t h e t o p - r ank e d t a r g e t a cr oss f i e l d s ($ R^2_test \sim 0.60-0.87$), while endogenous predictability rises markedly in neuro implants (R^2_test = 0.83), indicating that the quantum-computing asymmetry does not generalise uniformly. Case studies reveal that sharp entropy increases coincide with the opening of new conceptual frontiers, while entropy collapses signal technological convergence or paradigm displacement. These results demonstrate that conceptual diffusion is governed by stable structural regularities embedded in semantic and citation environments. By identifying early diversity-based signals of cross-domain uptake, the approach provides a scalable foundation for anticipatory scientometrics, technology foresight, and innovation-oriented policy analysis in rapidly evolving research fields.

19 pages, 5 figures, 6 tables. Code and manuscript sources: https://github.com/wazaahhh/breakthroughs-diffusion . An earlier version was presented at the Global Tech Mining Conference (GTM) 2026 (submission #117)