AI 中文总结
本研究开发公平基准测试方法，对比量子与经典机器学习模型在三类肿瘤数据集上的表现，未发现量子优势，建议优先分析高维度、更符合生物学实际的数据集以推进实用量子优势研究。
AI 中文摘要
机器学习正越来越多地用于癌症的检测、诊断与治疗，但由于生物数据具有高维度、样本多样性有限、特征交互复杂等特点，模型常难以应对这类数据。近期研究探索了量子机器学习模型在这类复杂数据上可能比经典模型表现更优的潜力，但往往缺乏对量子优势的严格实证评估。本研究基于Red Cedar量子机器学习与资源估计框架，结合AutoML优化的经典神经网络，开发了一套用于量子与经典机器学习模型公平基准测试的方法。我们对取自现有量子机器学习文献的表格型、组学及空间肿瘤数据集，采用多种预处理方法评估机器学习中的量子优势潜力，结果未发现量子优势的证据。我们的研究结果表明，该领域应优先分析更高维度、更符合生物学实际的数据集，以在肿瘤分类问题上取得实用量子优势的实质性进展。
英文摘要
Machine learning is being increasingly used for the detection, diagnosis, and treatment of cancer. However, models often struggle with biological data due to high dimensionality, limited sample diversity, and complex feature interactions. Recent works have investigated the potential for quantum machine learning models to exhibit improved performance over classical models on this kind of complex data, but have often lacked rigorous empirical evaluation of quantum advantage. In this work, we develop a methodology for fair benchmarking of quantum and classical machine learning models, based on the Red Cedar quantum machine learning and resource estimation framework and AutoML-optimized classical neural networks. We assess the potential for quantum advantage in machine learning across tabular, omics, and spatial oncological datasets drawn from the existing quantum machine learning literature, with a range of preprocessing methods, and find no evidence of quantum advantage. Our results suggest that the field should prioritize analyzing higher-dimensional, more biologically realistic datasets to make meaningful progress toward practical quantum advantage in oncological classification problems.
