# Quantum-Inspired Robust and Scalable SAR Object Classification

arXiv: https://arxiv.org/abs/2604.25755 · HTML: https://mlsanity.com/paper/2604.25755/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Builds on (most cited first, 8 of 8)

- [Learning Relevant Features of Data with Multi-scale Tensor Networks](https://mlsanity.com/paper/1801.00315/) (2017)
- [Supervised Learning with Quantum-Inspired Tensor Networks](https://mlsanity.com/paper/1605.05775/) (2016)
- [From probabilistic graphical models to generalized tensor networks for supervised learning](https://mlsanity.com/paper/1806.05964/) (2018)
- [Quantum-inspired Machine Learning on high-energy physics data](https://mlsanity.com/paper/2004.13747/) (2020)
- [Benchmarking Deep Learning Classifiers for SAR Automatic Target Recognition](https://mlsanity.com/paper/2312.06940/) (2023)
- [Riemannian Optimization on Tree Tensor Networks with Application in Machine Learning](https://mlsanity.com/paper/2507.21726/) (2025)
- [Exploring Deep Learning Methods for Classification of SAR Images: Towards NextGen Convolutions via Transformers](https://mlsanity.com/paper/2303.15852/) (2023)
- [SAR Object Detection with Self-Supervised Pretraining and Curriculum-Aware Sampling](https://mlsanity.com/paper/2504.13310/) (2025)

## Built on by (most cited first, 1 of 1)

- [Stochastic Optimization of Tree Tensor Networks](https://mlsanity.com/paper/2609.00870/) (2026)

## Abstract (arXiv)

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.