# A large scale statistical analysis of quantum and classical neural networks in the medical domain

## TL;DR

This paper compares classical and quantum neural networks for predicting heart disease, finding that quantum models perform well even with limited data.

## Contribution

The study introduces a structured methodology for evaluating quantum neural networks in medical prediction tasks.

## Key findings

- Quantum Neural Networks (QNNs) achieve comparable accuracy to classical models in heart disease prediction.
- QNNs show potential advantages in data-scarce scenarios, which are common in clinical settings.
- A reproducible evaluation framework for QNNs is presented, focusing on key design parameters.

## Abstract

Classical neural networks (NNs) have shown strong performance in medical data analysis. However, they typically require large labeled datasets and may struggle in data-scarce scenarios, common in clinical practice. Quantum Neural Networks (QNNs) have emerged as a promising alternative. This paper presents a comparative study between NNs and QNNs for heart disease prediction, addressing the limitations of current models in low-data regimes. We systematically evaluate 460 QNNs (using 11-13 qubits) and 4,480 NN architectures, analyzing key design parameters: encoding schemes, re-uploading strategies, circuit depth, and dropout (for QNNs), as well as hidden layers, neurons per layer, and dropout (for classical NNs). Top-performing models are selected for a direct comparison in terms of accuracy and sample complexity. Our results show QNNs achieve comparable accuracy and demonstrate potential advantages in data-scarce settings. Our study presents a structured and reproducible methodology for evaluating QNNs in clinical contexts, thereby supporting the broader investigation of quantum machine learning in applied healthcare domains.