Quantum Annealing Feature Selection on Light-weight Medical Image Datasets

• URL: http://arxiv.org/abs/2502.19201v1
• Date: Wed, 26 Feb 2025 15:09:23 GMT
• Title: Quantum Annealing Feature Selection on Light-weight Medical Image Datasets
• Authors: Merlin A. Nau, Luca A. Nutricati, Bruno Camino, Paul A. Warburton, Andreas K. Maier,
• Abstract summary: 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.<n>We present a method to solve larger feature selection instances than previously presented on commercial quantum annealers.<n>The method is tested in a toy problem where feature selection identifies pixel masks used to reconstruct small-scale medical images.
• Score: 8.813070033148023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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