# Quantum-Inspired Robust and Scalable SAR Object Classification

Source: [Quantum-Inspired Robust and Scalable SAR Object Classification](<https://www.sota2.com/research/paper/69f1909a55a3c32af89fbb27>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-26T05:00:05.667Z

Authors: Maximilian Scharf, Marco Trenti, Felix Bock, Padraig Davidson, Tobias Brosch, Benjamin Rodrigues de Miranda, Sigurd Huber, Timo Felser

Published: 2026-04-28T15:18:42.000Z

## Abstract

SAR image classification naturally has to deal with huge noise and a high dynamic range particularly requiring robust classification models. Additionally, the deployment of these models on edge devices, such as drones and military aircraft, requires a careful balance between model size and classification accuracy. This study explores the potential of tensor networks to meet these robustness requirements, specifically evaluating their resilience to data poisoning. Unlike previous works that concentrated on conventional neural networks for SAR object detection, this research focuses on the robustness and model reduction capabilities of tensor networks in object classification. Our findings indicate that tensor networks are adept at addressing both the challenges of robustness and the need for model efficiency, thereby contributing valuable insights to the ongoing discourse in radar applications and deep learning methodologies in general.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.25755>)

[PDF](<https://arxiv.org/pdf/2604.25755.pdf>)