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

arXiv: https://arxiv.org/abs/2608.20240 · HTML: https://mlsanity.com/paper/2608.20240/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 48 references that are in the map.
- First posted on arXiv 20 Aug 2026.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [Adversarial Attacks on Deep-Learning Based Radio Signal Classification](https://mlsanity.com/paper/1808.07713/) (2018)
- [Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems](https://mlsanity.com/paper/1902.08391/) (2019)
- [Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network](https://mlsanity.com/paper/2101.12090/) (2021)
- [Space-based Global Maritime Surveillance. Part I: Satellite Technologies](https://mlsanity.com/paper/2011.11304/) (2020)
- [Mapping Global Floods with 10 Years of Satellite Radar Data](https://mlsanity.com/paper/2411.01411/) (2024)
- [Black-box Adversarial ML Attack on Modulation Classification](https://mlsanity.com/paper/1908.00635/) (2019)
- [PAST-AI: Physical-layer Authentication of Satellite Transmitters via Deep Learning](https://mlsanity.com/paper/2010.05470/) (2020)
- [An Analysis of Complex-Valued CNNs for RF Data-Driven Wireless Device Classification](https://mlsanity.com/paper/2202.09777/) (2022)
- [Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2512.12002/) (2025)
- [Spotr: GPS Spoofing Detection via Device Fingerprinting](https://mlsanity.com/paper/2005.08787/) (2020)
- [Physical Layer Authentication for LEO Satellite Constellations](https://mlsanity.com/paper/2202.08757/) (2022)
- [On the Reliability of Radio Frequency Fingerprinting](https://mlsanity.com/paper/2408.09179/) (2024)
- [OrbID: Identifying Orbcomm Satellite RF Fingerprints](https://mlsanity.com/paper/2503.02118/) (2025)

## Abstract (arXiv)

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% of the training data to match the accuracy of classical baselines -- data collection being notoriously the most time-consuming phase of PLA; and, (ii) at an equal data budget, it improves classification accuracy over those baselines. In detail, we test our solution under three adversarial scenarios: replay, crafted-IQ injection, and space-borne spoofing. QUASAR rejects spoofed transmissions in 89.7%, 94.1%, and 81.3% of attempts, respectively, establishing the first quantum-enhanced physical-layer classifier for satellite constellations. The fully detailed framework and the supporting results, other than being interesting on their own, show a novel research avenue for physical-layer authentication.