mlsanityData to 14 Oct 2026

Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors

arXiv 2604.13951 ↗First posted 15 Apr 2026cs.LG quant-phcited by 0

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Abstract (arXiv)

This study evaluates colorectal risk factors and compares classical models against Quantum Neural Networks (QNNs) for anastomotic leak prediction. Analyzing clinical data with 14% leak prevalence, we tested ZZFeatureMap encodings with RealAmplitudes and EfficientSU2 ansatze under simulated noise. Fβ-optimized quantum configurations yielded significantly higher sensitivity (83.3%) than classical baselines (66.7%). This demonstrates that quantum feature spaces better prioritize minority class identification, which is critical for low-prevalence clinical risk prediction. Our work explores various optimizers under noisy conditions, highlighting key trade-offs and future directions for hardware deployment.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 56 references that are in the map. First posted on arXiv 15 Apr 2026.

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17 of 56 references.

References · 17

  1. 2020Variational Quantum Algorithmscited by 692
  2. 2018Supervised learning with quantum enhanced feature spacescited by 665
  3. 2020The power of quantum neural networkscited by 379
  4. 2020A rigorous and robust quantum speed-up in supervised machine learningcited by 271
  5. 2021Generalization in quantum machine learning from few training datacited by 216
  6. 2020Noise-Induced Barren Plateaus in Variational Quantum Algorithmscited by 193
  7. 2017Quantum machine learning: a classical perspectivecited by 95
  8. 2021The Inductive Bias of Quantum Kernelscited by 91
  9. 2022Group-Invariant Quantum Machine Learningcited by 56
  10. 2022Classical surrogates for quantum learning modelscited by 39
  11. 2025Optimization Strategies for Variational Quantum Algorithms in Noisy Landscapescited by 3
  12. 2023A Unitary Weights Based One-Iteration Quantum Perceptron Algorithm for Non-Ideal Training Setscited by 2
5 more
  1. 2025A Preliminary Investigation on the Usage of Quantum Approximate Optimization Algorithms for Test Case Selectioncited by 2
  2. 2026A Longitudinal Analysis of the CEC Single-Objective Competitions (2010-2024) and Implications for Variational Quantum Optimizationcited by 2
  3. 2024Identification and Mitigating Bias in Quantum Machine Learningcited by 1
  4. 2025Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studiescited by 1
  5. 2025From Classical to Hybrid: A Practical Framework for Quantum-Enhanced Learningcited by 1

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