A Classical-Quantum Convolutional Neural Network for Detecting Pneumonia from Chest Radiographs

TL;DR

This paper demonstrates that integrating a variational quantum circuit into a classical convolutional neural network enhances pneumonia detection accuracy from chest radiographs, showing promising potential for quantum machine learning in healthcare.

Contribution

It introduces a novel hybrid quantum-classical neural network architecture for medical image analysis and provides empirical evidence of its superior performance over classical models.

Findings

Hybrid network outperforms classical CNN on multiple metrics.

Performance improvements are statistically significant.

Quantum integration shows potential for real-world medical applications.

Abstract

While many quantum computing techniques for machine learning have been proposed, their performance on real-world datasets remains to be studied. In this paper, we explore how a variational quantum circuit could be integrated into a classical neural network for the problem of detecting pneumonia from chest radiographs. We substitute one layer of a classical convolutional neural network with a variational quantum circuit to create a hybrid neural network. We train both networks on an image dataset containing chest radiographs and benchmark their performance. To mitigate the influence of different sources of randomness in network training, we sample the results over multiple rounds. We show that the hybrid network outperforms the classical network on different performance measures, and that these improvements are statistically significant. Our work serves as an experimental demonstration of the potential of quantum computing to significantly improve neural network performance for real-world, non-trivial problems relevant to society and industry.