QUASAR: A Quantum-Classical Neural Network for SAR Satellite Physical-Layer Authentication

Authors: Vincenzo Sammartino, Nathanael Denis, Roberto Di Pietro
Published: 2026-08-20

Source: https://arxiv.org/abs/2608.20240

PDF: https://arxiv.org/pdf/2608.20240

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量子機械学習の役割 信号処理技術 データ収集手法

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Abstract

X-band SAR satellites (8-12 GHz) play a critical role in disaster response, environmental monitoring, and military intelligence. Yet, they lack robust physical-layer authentication (PLA), a security layer orthogonal to cryptographic solutions. Existing PLA systems, typically based on radio-frequency fingerprinting, are often limited to sub-6 GHz frequencies and rely on classical deep learning. However, this approach underfits the IQ phase nonlinearities that distinguish satellite hardware. In this paper, we present QUASAR, to the best of our knowledge the first quantum-classical hybrid architecture that fuses a CNN spectrogram encoder with a variational quantum circuit (VQC) to provide PLA to X-band SAR signals. Our solution enjoys two distinctive features: (i) it is markedly more data-efficient than classical machine learning, requiring only 10