AI 中文总结
该研究针对NISQ硬件，测试不同特征映射在三类噪声下对QSVM分类准确率的影响，发现振幅启发式特征映射噪声鲁棒性最优，为近量子硬件的特征映射选择提供了数据驱动标准。
AI 中文摘要
门级噪声会降低量子支持向量机（Quantum Support Vector Machine, QSVM）在含噪声中等规模量子（Noisy Intermediate-Scale Quantum, NISQ）硬件上的分类准确率，退化程度取决于经典数据编码为量子态的方式。我们在52项受控实验中，针对去极化噪声、比特翻转噪声和相位翻转噪声信道，测试了Z、ZZ、a Pauli及振幅启发式特征映射，误差概率p取值为0.01、0.05、0.10和0.50。振幅启发式特征映射在所有三类噪声信道下，当p达0.10时仍保持100%的测试准确率，而其他特征映射在相同噪声水平和类型下准确率降至65%-90%。研究发现Z特征映射对相位翻转噪声具有极强的免疫性，这是对易关系[R_Z, Z] = 0的结果。纠缠电路变体在噪声下的训练集与测试集间泛化差距最高达17.5%，而振幅变体始终保持零差距。这些结果为从业者提供了近量子硬件上选择特征映射的数据驱动标准。
英文摘要
Gate-level noise degrades the classification accuracy of Quantum Support Vector Machines (QSVMs) on Noisy Intermediate-Scale Quantum (NISQ) hardware, and the degree of degradation depends on how classical data is encoded into quantum states. We tested Z, ZZ, a Pauli, and an amplitude-inspired feature maps under depolarizing, bit-flip, and phase-flip noise channels in $52$ controlled experiments with error probabilities $p =0.01, 0.05, 0.10$, and $0.50$. The amplitude-inspired feature map had $100$\% test accuracy up to $p = 0.10$ across all three noise channels, while other feature maps fell to $65$-$90$\% under the same noise level and type. The Z feature map was found to be immune to phase-flip noise to a significantly high error rate, a consequence of the commutation relation $[R_Z, Z] = 0$. Entangled circuit variants produced generalization gaps in train-test sets of up to $17.5$\% under noise, whereas the amplitude variants maintained zero gap throughout. These results give practitioners data-driven criteria for a feature map on near-term quantum hardware.
Comments7 pages
Journal refIEEE Optoelectronics Global Conference (IEEE OGC), Shenzhen, China September 8-11, (2026)
