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.
@article{arxiv.2505.20797,
title = {Multi-VQC: A Novel QML Approach for Enhancing Healthcare Classification},
author = {Antonio Tudisco and Deborah Volpe and Giovanna Turvani},
journal= {arXiv preprint arXiv:2505.20797},
year = {2025}
}