Quantum Annealing Feature Selection on Light-weight Medical Image Datasets

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.

@article{nau2025_2502.19201,
  title={ Quantum Annealing Feature Selection on Light-weight Medical Image Datasets },
  author={ Merlin A. Nau and Luca A. Nutricati and Bruno Camino and Paul A. Warburton and Andreas K. Maier },
  journal={arXiv preprint arXiv:2502.19201},
  year={ 2025 }
}