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

arXiv: https://arxiv.org/abs/2604.13951 · HTML: https://mlsanity.com/paper/2604.13951/

## 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.

## Builds on (most cited first, 17 of 17)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [A rigorous and robust quantum speed-up in supervised machine learning](https://mlsanity.com/paper/2010.02174/) (2020)
- [Generalization in quantum machine learning from few training data](https://mlsanity.com/paper/2111.05292/) (2021)
- [Noise-Induced Barren Plateaus in Variational Quantum Algorithms](https://mlsanity.com/paper/2007.14384/) (2020)
- [Quantum machine learning: a classical perspective](https://mlsanity.com/paper/1707.08561/) (2017)
- [The Inductive Bias of Quantum Kernels](https://mlsanity.com/paper/2106.03747/) (2021)
- [Group-Invariant Quantum Machine Learning](https://mlsanity.com/paper/2205.02261/) (2022)
- [Classical surrogates for quantum learning models](https://mlsanity.com/paper/2206.11740/) (2022)
- [Optimization Strategies for Variational Quantum Algorithms in Noisy Landscapes](https://mlsanity.com/paper/2506.01715/) (2025)
- [A Unitary Weights Based One-Iteration Quantum Perceptron Algorithm for Non-Ideal Training Sets](https://mlsanity.com/paper/2309.14366/) (2023)
- [A Preliminary Investigation on the Usage of Quantum Approximate Optimization Algorithms for Test Case Selection](https://mlsanity.com/paper/2504.18955/) (2025)
- [A Longitudinal Analysis of the CEC Single-Objective Competitions (2010-2024) and Implications for Variational Quantum Optimization](https://mlsanity.com/paper/2603.24140/) (2026)
- [Identification and Mitigating Bias in Quantum Machine Learning](https://mlsanity.com/paper/2409.19011/) (2024)
- [Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies](https://mlsanity.com/paper/2506.19973/) (2025)
- [From Classical to Hybrid: A Practical Framework for Quantum-Enhanced Learning](https://mlsanity.com/paper/2511.08205/) (2025)

## 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.