# Forecasting Conceptual Diffusion in Science: The Case of Quantum Computing

arXiv: https://arxiv.org/abs/2606.03919 · HTML: https://mlsanity.com/paper/2606.03919/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 2 Jun 2026.

## Builds on (most cited first, 11 of 11)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts](https://mlsanity.com/paper/2205.01833/) (2022)
- [Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network](https://mlsanity.com/paper/2210.00881/) (2022)
- [Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics](https://mlsanity.com/paper/1906.06843/) (2019)
- [Forecasting high-impact research topics via machine learning on evolving knowledge graphs](https://mlsanity.com/paper/2402.08640/) (2024)
- [Invention as a Combinatorial Process: Evidence from U.S. Patents](https://mlsanity.com/paper/1406.2938/) (2014)
- [International Research Collaboration: Novelty, Conventionality, and Atypicality in Knowledge Recombination](https://mlsanity.com/paper/1804.09070/) (2018)
- [The Memory of Science: Inflation, Myopia, and the Knowledge Network](https://mlsanity.com/paper/1607.05606/) (2016)
- [Does diversity of papers affect their citations? Evidence from American Physical Society Journals](https://mlsanity.com/paper/1512.05057/) (2015)
- [Predicting Citation Counts with a Neural Network](https://mlsanity.com/paper/1806.04641/) (2018)

## Abstract (arXiv)

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 (R2 up to 0.78`a) and are driven by upstream heterogeneity, citation breadth, and distributional dispersion, as shown by SHAP analyses; replications on robotics, advanced materials, and neuro implants confirm that exogenous diffusion remains the top-ranked target across fields (R2test ~ 0.60-0.87), while endogenous predictability rises markedly in neuro implants (R2test = 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.