Quantum Annealing Feature Selection on Light-weight Medical Image Datasets

TL;DR

This paper explores using quantum annealing algorithms on real hardware to improve feature selection in lightweight medical image datasets, demonstrating scalability and effectiveness in simplified scenarios.

Contribution

It introduces a scalable quantum annealing method combining Ising penalties, subsampling, and thresholding for feature selection in medical imaging.

Findings

Quantum annealing effectively identifies pixel masks for image reconstruction.

The approach scales better than previous methods on quantum hardware.

Results show potential but hardware limitations restrict broader application.

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.