Nau Merlin A, Nutricati Luca A, Camino Bruno, Warburton Paul A, Maier Andreas K
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052, Erlangen, Germany.
London Centre of Nanotechnology, University College London, London, WC1H 0AH, UK.
Sci Rep. 2025 Aug 7;15(1):28937. doi: 10.1038/s41598-025-14611-x.
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. Quantum computers, particularly quantum annealers, are well-suited for such problems, which may offer advantages under certain problem formulations. We present a method to solve larger feature selection instances than previously demonstrated 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. We compare our approach against a range of feature selection strategies, including randomized baselines, classical supervised and unsupervised methods, combinatorial optimization via classical and quantum solvers, and learning-based feature representations. 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. While learned feature representations such as autoencoders achieve superior reconstruction performance, they do not offer the same level of interpretability or direct control over input feature selection as our approach.
我们研究在实际量子硬件上使用量子计算算法，以解决轻量级医学图像数据集特征选择这一计算密集型任务。特征选择通常被表述为一个n选k的选择问题，其中随着k和n的增加，复杂度呈二项式增长。量子计算机，特别是量子退火器，非常适合这类问题，在某些问题表述下可能具有优势。我们提出了一种方法，能够解决比之前在商业量子退火器上展示的更大规模的特征选择实例。我们的方法将线性伊辛罚分机制与子采样和阈值化技术相结合，以提高可扩展性。该方法在一个玩具问题中进行了测试，其中特征选择用于识别用于重建小尺度医学图像的像素掩码。我们将我们的方法与一系列特征选择策略进行了比较，包括随机基线、经典监督和无监督方法、通过经典和量子求解器进行的组合优化以及基于学习的特征表示。结果表明，基于量子退火的特征选择对于这个简化的用例是有效的，展示了其在高维优化任务中的潜力。然而，鉴于当前量子计算硬件的局限性，其在更广泛的现实世界问题中的适用性仍然不确定。虽然像自动编码器这样的基于学习的特征表示实现了卓越的重建性能，但它们在可解释性或对输入特征选择的直接控制方面，无法达到我们方法的水平。
