Sickness to prevention
Predict and prevent, with genomics and population health doing the predicting.
What the Plan commits to
- A genomics population health service, including newborn genomic testing ambitions.
- Precision medicine and earlier diagnosis pathways.
- Action on the leading preventable drivers: tobacco, obesity, alcohol.
- Core20PLUS5 as the equity lens on every prevention programme.
The modelling question
Prevention rests on two computational substrates: molecular-scale electronic structure (what a drug does) and graph structure over populations (who is at risk and through which connections). Both have quantum formulations that are testable on an emulator today.
Instruments that plausibly apply
Evidence already on this site
QPDE ethylene benchmark, 20/20 cells PASS, gap fit 0.8099 vs 0.8000 Ha
Read →H2-class noise ladder plus Richardson ZNE on that gap
Read →Laplacian moments separating two Weisfeiler-Leman-indistinguishable graphs
Read →The gap
Two qubits of ethylene is not drug discovery, and a 6-node graph is not a population. What these gates establish is that the estimator behaves as predicted under realistic noise — the precondition for scale, not scale itself.
Sources
- Fit for the Future: 10 Year Health Plan for England (DHSC, July 2025)
- Core20PLUS5 — reducing healthcare inequalities (NHS England)