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

Vojtěch Novák; Ivan Zelinka; Lenka Přibylová; Lubomír Martínek; Vladimír Benčurík; Martin Beseda

cs.LGApr 15, 2026

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

Authors: Vojtěch Novák, Ivan Zelinka, Lenka Přibylová, Lubomír Martínek, Vladimír Benčurík, Martin Beseda

Organizations: Department of Computer Science, Technical University of Ostrava, Czech Republic · IT4Innovations National Supercomputing Center, Ostrava, Czech Republic · Department of Applied Mathematics, Technical University of Ostrava, Czech Republic · Department of Surgical Studies, Faculty of Medicine, University of Ostrava, Czech Republic ·

Abstract

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. $F_β$ -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.

Figures & tables

Figure 1: Mean convergence of loss function values over 10 runs for RealAmplitudes (RA) and EfficientSU2 (ESU2) ansatze, optimized using different algorithms. The optimization includes sampling noise (1024 shots) and modeled quantum decoherence and gate errors.

Model	Threshold	AUC	Acc.	Sens.	Spec.	F1	Brier	Log Loss	Efron’s $R^{2}$
LR	0.186	0.740	85.0%	66.7%	88.2%	57.1%	0.118	0.390	0.072
AdaBoost	0.291	0.745	85.0%	66.7%	88.2%	57.1%	0.134	0.443	-0.054
LDA	0.180	0.750	80.0%	66.7%	82.4%	50.0%	0.115	0.381	0.095
GNB	0.231	0.809	90.0%	66.7%	94.1%	66.7%	0.116	0.393	0.087
MLP	0.176	0.765	90.0%	66.7%	94.1%	66.7%	0.120	0.403	0.059
CMA-ES (RA)	0.207	0.804	80.0%	83.3%	79.4%	55.6%	0.118	0.395	0.077

Table 1: Unified Performance Report across Classical and Quantum Models

Figure 2: Ranked Model AUC Performance with Bootstrapped 95% Stratified Confidence Intervals.

Figure 3: Receiver Operating Characteristic (ROC) comparing classical baselines against champion Quantum Neural Network configurations.

Figure 4: McNemar Agreement matrix for classification outputs. Highlighted cells designate significant divergence in model predictions ( $p<0.05$ ).

Clinical Outcome Prediction

Clinical outcome prediction uses patient records, imaging, signals, and biomarkers to estimate risks, disease trajectories, treatment response, and survival.

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Quantum Reservoir Computing with Neutral Atoms on a Small, Complex, Medical Dataset

Luke Antoncich, Yuben Moodley, Ugo Varetto, Jingbo Wang+4

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Quantum Reservoir Computing Clinical Outcome Prediction

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Biomarker-based prediction of clinical outcomes is challenging due to nonlinear relationships, correlated features, and the limited size of many medical datasets. Classical machine-learning methods can struggle under these conditions, motivating the search for alternatives. In this work, we investigate quantum reservoir computing (QRC), using both noiseless emulation and hardware execution on the neutral-atom Rydberg processor Aquila. We evaluate performance with six classical machine-learning models and use SHAP to generate feature subsets. We find that models trained on emulated quantum features achieve mean test accuracies comparable to those trained on classical features, but have higher training accuracies and greater variability over data splits, consistent with overfitting. When comparing hardware execution of QRC to noiseless emulation, the models are more robust over different data splits and often exhibit statistically significant improvements in mean test accuracy. This combination of improved accuracy and increased stability is suggestive of a regularising effect induced by hardware execution. To investigate the origin of this behaviour, we examine the statistical differences between hardware and emulated quantum feature distributions. We find that hardware execution applies a structured, time-dependent transformation characterised by compression toward the mean and a progressive reduction in mutual information relative to emulation.

Abstract

Clinical Outcome Prediction

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