量子机器学习在血细胞图像中检测白血病，性能接近经典模型。
Analyzing Images of Blood Cells with Quantum Machine Learning Methods: Equilibrium Propagation and Variational Quantum Circuits to Detect Acute Myeloid Leukemia
- 用无反向传播的平衡传播法和变分量子电路处理医学图像
- 仅用50~250张样本即达83%~86%准确率，比经典模型少需5倍数据
- 为当前量子计算条件下的医疗应用提供可复现的基准
本文开展可行性研究，证明量子机器学习（QML）算法在真实医学影像任务中虽受严重限制仍具竞争力。评估了不依赖反向传播的基于能量的学习方法——平衡传播（EP）与变分量子电路（VQC），用于从血液细胞显微图像中自动识别急性髓系白血病（AML）的二分类任务（AML vs. 健康）。关键结果：使用有限子集（每类50-250张图像）的AML-Cytomorphology数据集（共18,365张专家标注图像），在图像分辨率降至64×64像素、特征工程为20维且通过Qiskit进行经典模拟的前提下，量子方法性能仅比经典卷积神经网络（CNN）低12%-15%。平衡传播达到86.4%准确率（仅落后12%），而4量子比特的VQC在仅50样本/类时即保持83.0%稳定性能，相较之下，经典CNN需250样本才能达到98%准确率。这些结果建立了医疗领域量子机器学习的可复现基线，验证了近似量子时代（NISQ）的可行性。
原文摘要 · Abstract (English)
This paper presents a feasibility study demonstrating that quantum machine learning (QML) algorithms achieve competitive performance on real-world medical imaging despite operating under severe constraints. We evaluate Equilibrium Propagation (EP), an energy-based learning method that does not use backpropagation (incompatible with quantum systems due to state-collapsing measurements) and Variational Quantum Circuits (VQCs) for automated detection of Acute Myeloid Leukemia (AML) from blood cell microscopy images using binary classification (2 classes: AML vs. Healthy). Key Result: Using limited subsets (50-250 samples per class) of the AML-Cytomorphology dataset (18,365 expert-annotated images), quantum methods achieve performance only 12-15% below classical CNNs despite reduced image resolution (64x64 pixels), engineered features (20D), and classical simulation via Qiskit. EP reaches 86.4% accuracy (only 12% below CNN) without backpropagation, while the 4-qubit VQC attains 83.0% accuracy with consistent data efficiency: VQC maintains stable 83% performance with only 50 samples per class, whereas CNN requires 250 samples (5x more data) to reach 98%. These results establish reproducible baselines for QML in healthcare, validating NISQ-era feasibility.
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