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Matched but Not Better: An Equivalence-Tested Benchmark of Quantum Machine Learning on Six Clinical Datasets and the QML-Health-READY Reporting Framework
Not provided in the abstract
quantum machine learningclinical predictionequivalence testing
2611.05442
Builder Relevance
2h ago40%
Abstract
This study evaluates quantum machine learning models against classical models in clinical prediction, finding no significant performance advantage for quantum models.
Reality Card
Core Claim
Quantum classifiers did not outperform tuned classical models across six clinical datasets.
Method / Result
QSVM differed from the kernel-matched RBF SVM by -0.006 AUROC, equivalent to +/-0.02.
Limitations
Configuration errors could reverse conclusions, and finite-shot estimation was a significant practical cost.
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