Geometric and quantum kernel methods for predicting skeletal muscle outcomes in experimental chronic obstructive pulmonary disease
Azadeh Alavi, Hamidreza Khalili, Stanley M H Chan, Fatemeh Kouchmeshki, Muhammad Usman, Ross Vlahos · 2026
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Description
Azadeh Alavi and co-authors brought quantum-flavored math to a conservative test in 2026: predicting muscle loss in mice with lung disease. Skeletal muscle dysfunction accompanies chronic obstructive pulmonary disease, but preclinical cohorts are small, so fancy models must be tested carefully. The team studied over two hundred mice exposed to cigarette smoke, using blood and lung-wash biomarkers to predict thigh muscle weight, quality, and force.
Their method maps synthetic shape-based reference objects through a kernel space, compresses them with random projections fitted only on training data, and feeds low-dimensional modeled quantum regression circuits. It was benchmarked against classic ridge and kernel models under equal cross-checking folds. The study's value lies as much in its discipline as its winner: equal folds and conservative testing keep small-sample machine learning honest. A standard quantum methods must meet to be believed.
Catalog record
| CREATOR | Azadeh Alavi, Hamidreza Khalili, Stanley M H Chan, Fatemeh Kouchmeshki, Muhammad Usman, Ross Vlahos |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 97 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |