Practical Evaluation of Quantum Kernel Methods for Radar Micro-Doppler Classification on Noisy Intermediate-Scale Quantum (NISQ) Hardware

TL;DR

This study evaluates quantum kernel methods for radar micro-Doppler classification on NISQ hardware, demonstrating competitive performance and analyzing hardware noise effects, thus assessing their practical viability.

Contribution

It provides a systematic comparison of simulator and hardware QSVMs for radar classification, highlighting current limitations and potential of quantum kernel methods on NISQ devices.

Findings

QSVM achieves competitive accuracy with classical SVMs

Hardware noise impacts quantum kernel estimation

Newer quantum hardware shows improved stability

Abstract

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.