用量子机器学习分析脑影像数据，提升痴呆症分期准确率。
CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data
- 先用经典计算预处理和降维，再用量子支持向量机分类。
- 在阿尔茨海默病等脑影像数据上实现更高诊断准确率。
- 适合关注量子计算临床应用的研究者与医疗科技开发者。
本文提出CompressedMediQ，一种专为高维多类脑影像数据分析设计的混合量子-经典机器学习流程。针对阿尔茨海默病神经影像计划（ADNI）和额颞叶痴呆神经影像（NIFD）等大规模MRI数据集带来的计算挑战，该流程利用高性能计算节点进行先进影像预处理，并结合卷积神经网络与主成分分析（CNN-PCA）实现特征提取与降维，以应对当前噪声中等规模量子（NISQ）设备在量子态编码时的比特数量限制。随后采用量子支持向量机（QSVM）进行分类，通过量子核方法优化特征映射与分类性能，提升数据可分性。实验表明，该流程在痴呆症分期任务中显著优于传统方法，验证了量子增强学习在临床诊断中的实用性。尽管受限于NISQ设备，本研究仍证明了其变革潜力，为未来可扩展、精准的医疗诊断工具铺平道路。
原文摘要 · Abstract (English)
This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroimaging datasets, such as large-scale MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Neuroimaging in Frontotemporal Dementia (NIFD), present significant hurdles due to their vast size and complexity. CompressedMediQ integrates classical high-performance computing (HPC) nodes for advanced MRI pre-processing and Convolutional Neural Network (CNN)-PCA-based feature extraction and reduction, addressing the limited-qubit availability for quantum data encoding in the NISQ (Noisy Intermediate-Scale Quantum) era. This is followed by Quantum Support Vector Machine (QSVM) classification. By utilizing quantum kernel methods, the pipeline optimizes feature mapping and classification, enhancing data separability and outperforming traditional neuroimaging analysis techniques. Experimental results highlight the pipeline's superior accuracy in dementia staging, validating the practical use of quantum machine learning in clinical diagnostics. Despite the limitations of NISQ devices, this proof-of-concept demonstrates the transformative potential of quantum-enhanced learning, paving the way for scalable and precise diagnostic tools in healthcare and signal processing.
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