用量子支持向量机分类雷达微多普勒信号，实现在噪声硬件上高效准确识别目标。
Practical Evaluation of Quantum Kernel Methods for Radar Micro-Doppler Classification on Noisy Intermediate-Scale Quantum (NISQ) Hardware
- 先用PCA降维，再通过量子纠缠编码构建量子核空间进行分类
- 在133/156比特量子芯片上实现分类准确率媲美经典模型
- 揭示噪声和采样次数对量子核估计的影响，验证新架构稳定性
本文研究基于量子支持向量机（QSVM）的雷达航空目标分类方法，利用微多普勒特征进行识别。通过主成分分析（PCA）对经典特征进行降维，以提升量子编码效率。将降维后的特征向量通过全纠缠的ZZFeatureMap嵌入量子核特征空间，并采用核方法实现QSVM分类。性能首先在量子模拟器上评估，随后在真实NISQ时代超导量子硬件（IBM Torino 133-qubit 和 IBM Fez 156-qubit）上验证。实验表明，该方法在显著降低特征维度的前提下，仍可达到与经典SVM相当的分类性能。硬件测试揭示了噪声、退相干及测量采样次数对量子核估计的影响，并显示在新一代Heron r2架构上具有更高稳定性和保真度。本研究系统比较了仿真与硬件实现的差异，验证了量子核方法在实际雷达信号分类中的可行性与当前局限。
原文摘要 · Abstract (English)
This paper examines the application of a Quantum Support Vector Machine (QSVM) for radarbased aerial target classification using micro-Doppler signatures. Classical features are extracted and reduced via Principal Component Analysis (PCA) to enable efficient quantum encoding. The reduced feature vectors are embedded into a quantum kernel-induced feature space using a fully entangled ZZFeatureMap and classified using a kernel based QSVM. Performance is first evaluated on a quantum simulator and subsequently validated on NISQ-era superconducting quantum hardware, specifically the IBM Torino (133-qubit) and IBM Fez (156-qubit) processors. Experimental results demonstrate that the QSVM achieves competitive classification performance relative to classical SVM baselines while operating on substantially reduced feature dimensionality. Hardware experiments reveal the impact of noise and decoherence and measurement shot count on quantum kernel estimation, and further show improved stability and fidelity on newer Heron r2 architecture. This study provides a systematic comparison between simulator-based and hardware-based QSVM implementations and highlights both the feasibility and current limitations of deploying quantum kernel methods for practical radar signal classification tasks.
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