Quantum machine learning framework for longitudinal biomedical studies
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913807611199488 |
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| author | Demidik, Maria Utro, Filippo Galda, Alexey Jansen, Karl Blankenberg, Daniel Parida, Laxmi |
| author_facet | Demidik, Maria Utro, Filippo Galda, Alexey Jansen, Karl Blankenberg, Daniel Parida, Laxmi |
| contents | Longitudinal biomedical studies play a vital role in tracking disease progression, treatment response, and the emergence of resistance mechanisms, particularly in complex disorders such as cancer and neurodegenerative diseases. However, the high dimensionality of biological data, combined with the limited size of longitudinal cohorts, presents significant challenges for traditional machine learning approaches. In this work, we explore the potential of quantum machine learning (QML) for longitudinal biomarker discovery. We propose a novel modification to the instantaneous quantum polynomial time (IQP) feature map, designed to encode temporal dependencies across multiple time points in biomedical datasets. Through numerical simulations on both synthetic and real-world datasets - including studies on follicular lymphoma and Alzheimer's disease - we demonstrate that our longitudinal IQP feature map improves the ability of quantum kernels to capture intra-subject temporal patterns, offering a promising direction for QML in clinical research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18392 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum machine learning framework for longitudinal biomedical studies Demidik, Maria Utro, Filippo Galda, Alexey Jansen, Karl Blankenberg, Daniel Parida, Laxmi Quantitative Methods Quantum Physics Longitudinal biomedical studies play a vital role in tracking disease progression, treatment response, and the emergence of resistance mechanisms, particularly in complex disorders such as cancer and neurodegenerative diseases. However, the high dimensionality of biological data, combined with the limited size of longitudinal cohorts, presents significant challenges for traditional machine learning approaches. In this work, we explore the potential of quantum machine learning (QML) for longitudinal biomarker discovery. We propose a novel modification to the instantaneous quantum polynomial time (IQP) feature map, designed to encode temporal dependencies across multiple time points in biomedical datasets. Through numerical simulations on both synthetic and real-world datasets - including studies on follicular lymphoma and Alzheimer's disease - we demonstrate that our longitudinal IQP feature map improves the ability of quantum kernels to capture intra-subject temporal patterns, offering a promising direction for QML in clinical research. |
| title | Quantum machine learning framework for longitudinal biomedical studies |
| topic | Quantitative Methods Quantum Physics |
| url | https://arxiv.org/abs/2504.18392 |