Year: 2025
Type: conference-paper
Abstract: Accurate and reliable diagnosis of diseases is crucial in enabling timely medical treatment and enhancing patient survival rates. In recent years, Machine Learning has revolutionized diagnostic practices by creating classification models capable of identifying diseases. However, these classification problems often suffer from significant class imbalances, which can inhibit the effectiveness of traditional models. Therefore, the interest in Quantum models has arisen, driven by the captivating promise of overcoming the limitations of the classical counterpart thanks to their ability to express complex patterns by mapping data in a higher-dimensional computational space. This work proposes a novel approach for enhancing the classification performance of Quantum Neural Networks (QNN) consisting of multiple Variational Quantum Circuits (VQCs) arranged sequentially. This strategy increases the nonlinearity of the model by exploiting the measurement operation and improving its ability to capture complex patterns. In this analysis, the proposed method is compared against classical models while varying its degrees of freedom, specifically the number of involved VQCs, on three well-known healthcare datasets - Prostate Cancer, Heart Failure, and Diabetes. The results prove the potential of the quantum model and demonstrate the validity of the proposed approach, showing that its advantage increases with the complexity of the classification.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4413823257