Exploring Hybrid Quantum-Classical Machine Learning for Respiratory Sound Analysis

Respiratory sound analysis plays a crucial role in the detection and diagnosis of diseases. With advances in digital signal processing and artificial intelligence techniques, the automated classification of these sounds has become an active area of research. The objective of this work is to analyze the behavior of emerging hybrid quantum-classical machine learning models, which combine the robust capabilities of classical learning algorithms with the potential of quantum variational circuits. To accomplish this, we undertake a comparative analysis between Quantum Support Vector Machines and Quantum Neural Networks, juxtaposed with the equivalent classical AI models, employing various signal representation techniques. Based on the experimental results, integrating quantum techniques into the analysis of respiratory sounds enhances the effectiveness of classical methods, particularly in scenarios with limited examples. This is a common scenario in this context, where data availability is typically constrained.