Quantum Annealing Feature Selection on Light-weight Medical Image Datasets
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909512460402688 |
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| author | Nau, Merlin A. Nutricati, Luca A. Camino, Bruno Warburton, Paul A. Maier, Andreas K. |
| author_facet | Nau, Merlin A. Nutricati, Luca A. Camino, Bruno Warburton, Paul A. Maier, Andreas K. |
| contents | We investigate the use of quantum computing algorithms on real quantum hardware to tackle the computationally intensive task of feature selection for light-weight medical image datasets. Feature selection is often formulated as a k of n selection problem, where the complexity grows binomially with increasing k and n. As problem sizes grow, classical approaches struggle to scale efficiently. Quantum computers, particularly quantum annealers, are well-suited for such problems, offering potential advantages in specific formulations. We present a method to solve larger feature selection instances than previously presented on commercial quantum annealers. Our approach combines a linear Ising penalty mechanism with subsampling and thresholding techniques to enhance scalability. The method is tested in a toy problem where feature selection identifies pixel masks used to reconstruct small-scale medical images. The results indicate that quantum annealing-based feature selection is effective for this simplified use case, demonstrating its potential in high-dimensional optimization tasks. However, its applicability to broader, real-world problems remains uncertain, given the current limitations of quantum computing hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19201 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Annealing Feature Selection on Light-weight Medical Image Datasets Nau, Merlin A. Nutricati, Luca A. Camino, Bruno Warburton, Paul A. Maier, Andreas K. Quantum Physics Machine Learning We investigate the use of quantum computing algorithms on real quantum hardware to tackle the computationally intensive task of feature selection for light-weight medical image datasets. Feature selection is often formulated as a k of n selection problem, where the complexity grows binomially with increasing k and n. As problem sizes grow, classical approaches struggle to scale efficiently. Quantum computers, particularly quantum annealers, are well-suited for such problems, offering potential advantages in specific formulations. We present a method to solve larger feature selection instances than previously presented on commercial quantum annealers. Our approach combines a linear Ising penalty mechanism with subsampling and thresholding techniques to enhance scalability. The method is tested in a toy problem where feature selection identifies pixel masks used to reconstruct small-scale medical images. The results indicate that quantum annealing-based feature selection is effective for this simplified use case, demonstrating its potential in high-dimensional optimization tasks. However, its applicability to broader, real-world problems remains uncertain, given the current limitations of quantum computing hardware. |
| title | Quantum Annealing Feature Selection on Light-weight Medical Image Datasets |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2502.19201 |