# A hybrid quantum image edge detector for the NISQ era

arXiv: https://arxiv.org/abs/2203.12072 · HTML: https://mlsanity.com/paper/2203.12072/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 2 of 2)

- [Improved FRQI on superconducting processors and its restrictions in the NISQ era](https://mlsanity.com/paper/2110.15672/) (2021)
- [Quantum Edge Detection for Image Segmentation in Optical Environments](https://mlsanity.com/paper/1409.2918/) (2014)

## Built on by (most cited first, 8 of 8)

- [Hybrid quantum transfer learning for crack image classification on NISQ hardware](https://mlsanity.com/paper/2307.16723/) (2023)
- [Physics-inspired Generative AI models via real hardware-based noisy quantum diffusion](https://mlsanity.com/paper/2505.22193/) (2025)
- [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)
- [Configurable Algorithms for Histopathologic Cancer Detection on Quantum Hardware](https://mlsanity.com/paper/2606.21752/) (2026)
- [Training-free Quantum-Inspired Image Edge Extraction Method](https://mlsanity.com/paper/2501.18929/) (2025)
- [Hybrid Quantum-Classical AI for Industrial Defect Classification in Welding Images](https://mlsanity.com/paper/2603.28995/) (2026)
- [Quantum Gradient-Based Approach for Edge and Corner Detection Using Sobel Kernels](https://mlsanity.com/paper/2605.00744/) (2026)
- [Quantum Diffusion Models for Medical Image Analysis](https://mlsanity.com/paper/2609.31070/) (2026)

## Abstract (arXiv)

Edges are image locations where the gray value intensity changes suddenly. They are among the most important features to understand and segment an image. Edge detection is a standard task in digital image processing, solved for example using filtering techniques. However, the amount of data to be processed grows rapidly and pushes even supercomputers to their limits. Quantum computing promises exponentially lower memory usage in terms of the number of qubits compared to the number of classical bits. In this paper, we propose a hybrid method for quantum edge detection based on the idea of a quantum artificial neuron. Our method can be practically implemented on quantum computers, especially on those of the current noisy intermediate-scale quantum era. We compare six variants of the method to reduce the number of circuits and thus the time required for the quantum edge detection. Taking advantage of the scalability of our method, we can practically detect edges in images considerably larger than reached before.