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We analysed a cigarette-smoke mouse model of experimental COPD comprising 213 animals with blood and bronchoalveolar-lavage biomarkers to predict tibialis anterior muscle weight, muscle quality, and force. We developed a kernel-geometric quantum hybrid method in which synthetic symmetric positive definite (SPD) references are mapped through a reproducing-kernel Hilbert space, compressed using train-only random projection, normalised, and supplied to low-dimensional simulated quantum regression circuits. We benchmarked this approach against classical Ridge\/kernel models, SPD relational representations, and quantum-kernel regression using identical condition-stratified repeated cross-validation folds. Results were endpoint-specific. Biomarker-only Ridge had the lowest RMSE for force, indicating that a compact linear model was sufficient for this endpoint in the present cohort. Synth_ROSE had the numerically lowest RMSE for muscle weight and muscle quality, but paired fold-level testing did not establish statistically significant superiority after Holm adjustment. These findings support leakage-controlled, endpoint-specific benchmarking of SPD and simulated quantum feature maps, not claims of clinical readiness, quantum hardware advantage, or definitive superiority over classical learning.<\/jats:p>","DOI":"10.1038\/s41598-026-65964-w","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T15:24:06Z","timestamp":1789053846000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Geometric and quantum kernel methods for predicting skeletal muscle outcomes in experimental chronic obstructive pulmonary disease"],"prefix":"10.1038","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9565-217X","authenticated-orcid":false,"given":"Azadeh","family":"Alavi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamidreza","family":"Khalili","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stanley M. 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