用量子机器学习分析基因组数据，发现特征映射影响分类效果。
Modeling Quantum Machine Learning for Genomic Data Analysis
- 用不同特征映射技术测试量子支持向量机和量子神经网络
- 量子神经网络训练准确率最高，但特征映射选择影响结果稳定性
- 适合对量子计算与生物信息交叉研究感兴趣的读者
量子机器学习（QML）持续发展，为多样化应用带来新机遇。本研究通过多种特征映射技术，评估了QML模型在基因组序列二分类任务中的适用性。我们基于Qiskit开源实现，在基准基因组数据集上开展实验。模拟结果表明，特征映射技术与QML算法的相互作用显著影响性能表现。其中，Pegasos量子支持向量分类器（Pegasos-QSVC）表现出高敏感性，尤其在召回率指标上表现优异；而量子神经网络（QNN）在所有特征映射下均达到最高训练准确率。然而，分类器性能在不同特征映射下的显著差异，暴露出对局部输出分布过拟合的风险。该工作凸显了QML在基因组数据分析中的变革潜力，同时强调需进一步提升方法的鲁棒性与准确性。
原文摘要 · Abstract (English)
Quantum Machine Learning (QML) continues to evolve, unlocking new opportunities for diverse applications. In this study, we investigate and evaluate the applicability of QML models for binary classification of genome sequence data by employing various feature mapping techniques. We present an open-source, independent Qiskit-based implementation to conduct experiments on a benchmark genomic dataset. Our simulations reveal that the interplay between feature mapping techniques and QML algorithms significantly influences performance. Notably, the Pegasos Quantum Support Vector Classifier (Pegasos-QSVC) exhibits high sensitivity, particularly excelling in recall metrics, while Quantum Neural Networks (QNN) achieve the highest training accuracy across all feature maps. However, the pronounced variability in classifier performance, dependent on feature mapping, highlights the risk of overfitting to localized output distributions in certain scenarios. This work underscores the transformative potential of QML for genomic data classification while emphasizing the need for continued advancements to enhance the robustness and accuracy of these methodologies.
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