# Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation

arXiv: https://arxiv.org/abs/2311.12912 · HTML: https://mlsanity.com/paper/2311.12912/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 91 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 18 of 18)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering](https://mlsanity.com/paper/2007.09990/) (2020)
- [Q-Match: Iterative Shape Matching via Quantum Annealing](https://mlsanity.com/paper/2105.02878/) (2021)
- [A practical heuristic for finding graph minors](https://mlsanity.com/paper/1406.2741/) (2014)
- [Adiabatic Quantum Computing for Multi Object Tracking](https://mlsanity.com/paper/2202.08837/) (2022)
- [FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation](https://mlsanity.com/paper/2304.13672/) (2023)
- [Quantum Motion Segmentation](https://mlsanity.com/paper/2203.13185/) (2022)
- [CCuantuMM: Cycle-Consistent Quantum-Hybrid Matching of Multiple Shapes](https://mlsanity.com/paper/2303.16202/) (2023)
- [Penalty Weights in QUBO Formulations: Permutation Problems](https://mlsanity.com/paper/2206.11040/) (2022)
- [Compound Figure Separation of Biomedical Images with Side Loss](https://mlsanity.com/paper/2107.08650/) (2021)
- [GCS-Q: Quantum Graph Coalition Structure Generation](https://mlsanity.com/paper/2212.11372/) (2022)
- [Quantum Optimisation of Complex Systems with a Quantum Annealer](https://mlsanity.com/paper/2105.13945/) (2021)
- [BILP-Q: Quantum Coalition Structure Generation](https://mlsanity.com/paper/2204.13802/) (2022)
- [Graph Cut Segmentation Methods Revisited with a Quantum Algorithm](https://mlsanity.com/paper/1812.03050/) (2018)
- [Inverse Quantum Fourier Transform Inspired Algorithm for Unsupervised Image Segmentation](https://mlsanity.com/paper/2301.04705/) (2023)
- [On the Importance of Feature Representation for Flood Mapping using Classical Machine Learning Approaches](https://mlsanity.com/paper/2303.00691/) (2023)

## Built on by (most cited first, 11 of 11)

- [Qubit-efficient Variational Quantum Algorithms for Image Segmentation](https://mlsanity.com/paper/2405.14405/) (2024)
- [Quantum-enhanced Computer Vision: Going Beyond Classical Algorithms](https://mlsanity.com/paper/2510.07317/) (2025)
- [Quantum Artificial Intelligence: A Brief Survey](https://mlsanity.com/paper/2408.10726/) (2024)
- [Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation](https://mlsanity.com/paper/2410.10713/) (2024)
- [Toward Quantum Utility in Finance: A Robust Data-Driven Algorithm for Asset Clustering](https://mlsanity.com/paper/2509.07766/) (2025)
- [Quantum-Assisted Correlation Clustering](https://mlsanity.com/paper/2509.03561/) (2025)
- [Quantum Feature Pyramid Gating for Seismic Image Segmentation](https://mlsanity.com/paper/2605.15370/) (2026)
- [Toward Near-Real-Time Marine Oil Spill Detection in SAR Imagery using Quantum-Assisted SVM](https://mlsanity.com/paper/2605.17217/) (2026)
- [QuantumQUBO Agent: Automating Quadratic Unconstrained Binary Optimization (QUBO) Formulation Generation from Natural Language](https://mlsanity.com/paper/2609.10629/) (2026)
- [Quantum-Gated LiteSSD: A Parameter-Efficient Lightweight Hybrid Quantum-Classical Framework for Forward-Looking Sonar Object Detection](https://mlsanity.com/paper/2609.14025/) (2026)
- [QINA: Quantum-Inspired Nonlinear Adapters for Pretrained Vision Models](https://mlsanity.com/paper/2609.29592/) (2026)

## Abstract (arXiv)

We present Q-Seg, a novel unsupervised image segmentation method based on quantum annealing, tailored for existing quantum hardware. We formulate the pixel-wise segmentation problem, which assimilates spectral and spatial information of the image, as a graph-cut optimization task. Our method efficiently leverages the interconnected qubit topology of the D-Wave Advantage device, offering superior scalability over existing quantum approaches and outperforming several tested state-of-the-art classical methods. Empirical evaluations on synthetic datasets have shown that Q-Seg has better runtime performance than the state-of-the-art classical optimizer Gurobi. The method has also been tested on earth observation image segmentation, a critical area with noisy and unreliable annotations. In the era of noisy intermediate-scale quantum, Q-Seg emerges as a reliable contender for real-world applications in comparison to advanced techniques like Segment Anything. Consequently, Q-Seg offers a promising solution using available quantum hardware, especially in situations constrained by limited labeled data and the need for efficient computational runtime.