提出新型归一化方法，提升量子-经典混合模型抗癌药效预测稳定性
Optimal normalization in quantum-classical hybrid models for anti-cancer drug response prediction
- 用改进的tanh梯度函数实现神经网络输出归一化，避免极端值集中
- 在癌细胞系基因表达数据上，优化后量子模型预测性能优于传统模型
- 适合需要小样本高泛化能力的生物医药数据分析场景
量子-经典混合机器学习（QHML）模型因在小样本数据下表现出强鲁棒性和高泛化能力，特别适用于抗癌药效预测这类样本有限的任务。然而，这类模型对神经网络与量子电路接口处的数据编码极为敏感，不当选择会导致训练不稳定。为此，本文提出一种基于修正梯度tanh函数的新归一化策略，有效避免神经网络输出过度集中于极值区间。在包含多种癌细胞系基因表达和药物响应数据的公开数据集上，对比了经典深度学习模型与多个QHML模型的预测表现。结果表明，在最优归一化条件下，QHML模型显著优于经典模型。该研究为量子计算机在生物医学数据分析中的应用提供了新路径。
原文摘要 · Abstract (English)
Quantum-classical Hybrid Machine Learning (QHML) models are recognized for their robust performance and high generalization ability even for relatively small datasets. These qualities offer unique advantages for anti-cancer drug response prediction, where the number of available samples is typically small. However, such hybrid models appear to be very sensitive to the data encoding used at the interface of a neural network and a quantum circuit, with suboptimal choices leading to stability issues. To address this problem, we propose a novel strategy that uses a normalization function based on a moderated gradient version of the $\tanh$. This method transforms the outputs of the neural networks without concentrating them at the extreme value ranges. Our idea was evaluated on a dataset of gene expression and drug response measurements for various cancer cell lines, where we compared the prediction performance of a classical deep learning model and several QHML models. These results confirmed that QHML performed better than the classical models when data was optimally normalized. This study opens up new possibilities for biomedical data analysis using quantum computers.
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