Optimized quantum classifiers do not beat AutoML-tuned classical neural networks on standard oncological benchmarks, and resource estimates indicate the datasets are too small to make quantum advantage plausible.
Artificial intelligence in oncology: Current landscape, challenges, and future directions,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
quant-ph 1years
2026 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Benchmarking Quantum and Classical Machine Learning Models on Oncological Data
Optimized quantum classifiers do not beat AutoML-tuned classical neural networks on standard oncological benchmarks, and resource estimates indicate the datasets are too small to make quantum advantage plausible.