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.

翻译：本文提出一项可行性研究，证明量子机器学习算法在现实世界医学影像分析中，即使在严格约束条件下仍能取得具有竞争力的性能。我们评估了基于能量的学习方法平衡传播（该算法不使用反向传播，因其测量会导致量子态坍缩，故与量子系统不兼容）以及变分量子电路，通过二分类（两类：急性髓系白血病 vs. 健康）实现血细胞显微图像中急性髓系白血病的自动检测。关键结果：使用AML细胞形态学数据集（包含18,365张专家标注图像）的有限子集（每类50-250个样本），在降低图像分辨率（64×64像素）、采用工程特征（20维）并通过Qiskit进行经典模拟的条件下，量子方法的性能仅比经典卷积神经网络低12-15%。平衡传播在不使用反向传播的情况下达到86.4%准确率（仅比卷积神经网络低12%），而4量子比特变分量子电路获得83.0%准确率且数据效率稳定：变分量子电路仅需每类50个样本即可保持83%的稳定性能，而卷积神经网络需要250个样本（数据量增加5倍）才能达到98%。这些结果为医疗健康领域的量子机器学习建立了可复现的基准，验证了含噪声中等规模量子计算时代的可行性。