A comparative evaluation of quantum machine learning architectures for breast cancer classification using clinical and genomic data

IntroductionIn recent years, high-dimensional clinical and genomic data have gained significant importance for prognosis and personalized medicine in breast cancer. But the use of quantum machine learning (QML) on such data is limited by the availability of few qubits, the computation time of quantum simulation, and dimensionality reduction. This work systematically compares several QML architectures for breast cancer classification in the presence of realistic and simulator constraints.MethodsT

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ethics.ai (29 July 2026), “A comparative evaluation of quantum machine learning architectures for breast cancer classification using clinical and genomic data,” evidence record 14642, https://ethics.ai/record/14642 (originally published by Frontiers in Artificial Intelligence).

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