FAU team reports 91.9% hardware accuracy on a small-data lung-cancer classifier

The headline accuracy rests on 309 real records expanded to 540 with synthetic samples, a limitation the release itself states.

Observed 2026-11-02Research
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Editorial note

Models were trained on 108 of the 540 augmented samples, and the source explicitly says the synthetic rows are not newly recruited patients; the dataset contains no imaging, pathology, laboratory values or staging. That makes 94.31% in simulation and 91.90% on annealing hardware a statement about one prepared dataset rather than about clinical performance. The result is worth recording precisely because the paper's own caveats are stronger than the announcement's headline.

Original sources

D-Wave Quantum

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