Multi-VQC: A Novel QML Approach for Enhancing Healthcare Classification
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908683008475136 |
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| author | Tudisco, Antonio Volpe, Deborah Turvani, Giovanna |
| author_facet | Tudisco, Antonio Volpe, Deborah Turvani, Giovanna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20797 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-VQC: A Novel QML Approach for Enhancing Healthcare Classification Tudisco, Antonio Volpe, Deborah Turvani, Giovanna Machine Learning Emerging Technologies 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. |
| title | Multi-VQC: A Novel QML Approach for Enhancing Healthcare Classification |
| topic | Machine Learning Emerging Technologies |
| url | https://arxiv.org/abs/2505.20797 |