Noise Resilience of Quantum Support Vector Machine with Selected Feature Maps

• URL: http://arxiv.org/abs/2608.17495v1
• Date: Tue, 18 Aug 2026 08:23:53 GMT
• Title: Noise Resilience of Quantum Support Vector Machine with Selected Feature Maps
• Abstract summary: Gate-level noise degrades the classification accuracy of QSVMs on NISQ hardware.<n>We tested Z, ZZ, a Pauli, and an amplitude-inspired feature maps under depolarizing, bit-flip, and phase-flip noise channels.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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