# Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation

arXiv: https://arxiv.org/abs/2410.10713 · HTML: https://mlsanity.com/paper/2410.10713/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Quantum-Inspired Machine Learning: a Survey](https://mlsanity.com/paper/2308.11269/) (2023)
- [Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation](https://mlsanity.com/paper/2311.12912/) (2023)
- [A hybrid quantum image edge detector for the NISQ era](https://mlsanity.com/paper/2203.12072/) (2022)
- [Qubit-efficient Variational Quantum Algorithms for Image Segmentation](https://mlsanity.com/paper/2405.14405/) (2024)
- [Hybrid quantum transfer learning for crack image classification on NISQ hardware](https://mlsanity.com/paper/2307.16723/) (2023)

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

- [Boosting Classification with Quantum-Inspired Augmentations](https://mlsanity.com/paper/2506.22241/) (2025)

## Abstract (arXiv)

Exploring the potential of quantum hardware for enhancing classical and real-world applications is an ongoing challenge. This study evaluates the performance of quantum and quantum-inspired methods compared to classical models for crack segmentation. Using annotated gray-scale image patches of concrete samples, we benchmark a classical mean Gaussian mixture technique, a quantum-inspired fermion-based method, Q-Seg a quantum annealing-based method, and a U-Net deep learning architecture. Our results indicate that quantum-inspired and quantum methods offer a promising alternative for image segmentation, particularly for complex crack patterns, and could be applied in near-future applications.