Comparing artificial neural networks with variational quantum circuits for biomedical data classification

This study presents a comparative evaluation of Artificial Neural Networks (ANNs) and Variational Quantum Circuits (VQCs) for biomedical classification, using the Wisconsin Breast Cancer Diagnostic dataset. Both models were optimized using Bayesian hyperparameter tuning via Optuna to ensure a fair performance comparison. The ANN achieved high predictive accuracy (98.2%), F1 score (98.2%), and Area Under the Curve (AUC) (0.98), exhibiting stable convergence and efficient training. The VQC, though trained under classical simulation, attained a respectable accuracy of 86.5% and an AUC of 0.85, with notably strong recall (99.1%) for malignant cases, highlighting its potential in scenarios requiring high sensitivity. Loss curves, confusion matrices, and hyperparameter importance visualizations were used to interpret each model’s training behaviour and decision boundaries. While classical models remain superior in current biomedical classification tasks, VQCs offer promising computational advantages and potential scalability for complex, high-dimensional datasets. This work provides early benchmarks for quantum-classical comparisons in biomedical machine learning and offers guidance for future implementations as quantum hardware becomes more accessible.