Quantum Annealing Feature Selection on Light-weight Medical Image Datasets
Journal:
arXiv
Published Date:
Feb 26, 2025
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.
