研究量子噪声对基因组分类中关键算法的影响，发现特征映射选择至关重要。
Modeling Feature Maps for Quantum Machine Learning
- 系统测试多种噪声对量子算法和特征映射的影响
- QSVC抗噪性强，但PauliFeatureMap易受干扰导致分类失效
- 为基因组分类中的量子机器学习提供选型指导
量子机器学习（QML）在基因组序列分类等复杂任务中潜力巨大，但嘈杂中尺度量子（NISQ）设备上的量子噪声带来实际挑战。本研究系统评估了去相位、振幅阻尼、去极化、热噪声、比特翻转和相位翻转等多种噪声模型对主要QML算法（QSVC、Peg-QSVC、QNN、VQC）及特征映射技术（ZFeatureMap、ZZFeatureMap、PauliFeatureMap）的影响。结果表明，QSVC在噪声下表现稳健，而Peg-QSVC和QNN对去极化与振幅阻尼噪声更为敏感；尤其保罗特征映射（PauliFeatureMap）在噪声条件下显著降低分类精度。这些发现强调了特征映射选择与噪声缓解策略在优化基因组分类中的关键作用，对未来个性化医疗具有重要意义。
原文摘要 · Abstract (English)
Quantum Machine Learning (QML) offers significant potential for complex tasks like genome sequence classification, but quantum noise on Noisy Intermediate-Scale Quantum (NISQ) devices poses practical challenges. This study systematically evaluates how various quantum noise models including dephasing, amplitude damping, depolarizing, thermal noise, bit-flip, and phase-flip affect key QML algorithms (QSVC, Peg-QSVC, QNN, VQC) and feature mapping techniques (ZFeatureMap, ZZFeatureMap, and PauliFeatureMap). Results indicate that QSVC is notably robust under noise, whereas Peg-QSVC and QNN are more sensitive, particularly to depolarizing and amplitude-damping noise. The PauliFeatureMap is especially vulnerable, highlighting difficulties in maintaining accurate classification under noisy conditions. These findings underscore the critical importance of feature map selection and noise mitigation strategies in optimizing QML for genomic classification, with promising implications for personalized medicine.
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