Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Journal of Theoretical Physics

Quantum Circuit Realization of Morphological Gradient for Quantum Grayscale Image

Ping Fan, Ri‐Gui Zhou, WenWen Hu, Naihuan Jing

Published Nov 16, 2018• 12 citations
Publisher
DOI
10.1007/s10773-018-3943-8

Related papers

Novel Design of Quantum Circuits for Representation of Grayscale Images

Mayukh Sarkar

arXiv:2302.07089•Lecture notes in networks and systems•Jan 1, 2023•2 citations

Quantum circuit design for several morphological image processing methods

Panchi Li, Tong Shi, Aiping Lu +1 more

Quantum Information Processing•Oct 26, 2019•43 citations

Quantum Image Morphology Processing Based on Quantum Set Operation

Ri‐Gui Zhou, Zhi-bo Chang, Ping Fan +2 more

International Journal of Theoretical Physics•Nov 19, 2014•18 citations

Quantum Image Edge Detection Based on Multi-Directions Gray-Scale Morphology

Wan-Xiu Li, Ri‐Gui Zhou, Han Jen Yu

International Journal of Theoretical Physics•Oct 9, 2021•7 citations

Detection of steganography in quantum grayscale images

Jia Luo, Ri‐Gui Zhou, WenWen Hu +2 more

Quantum Information Processing•Mar 24, 2020•12 citations

A Fully Quantum Algorithm for Image Edge Detection

Fred Sun

This work introduces a novel quantum algorithm for gradient-based edge detection that operates entirely within the quantum circuit model. Grayscale images are encoded using the Novel Enhanced Quantum Representation (NEQR), allowing exact arithmetic on pixel intensities. Directional gradients are computed by generating superpositions of neighboring pixels via cyclic shift operations and performing subtraction with an exact quantum arithmetic circuit. To refine accuracy, we introduce a direction-aware shifting mechanism that aligns edges with the darker side of intensity transitions. Our novel Quantum Partitioning Algorithm enables efficient in-place thresholding of edge candidates. This work exhibits polynomial-time improvements and optimizes the ancilla count compared to previous NEQR-based quantum edge detection algorithms. These results demonstrate a resource-efficient and fully quantum approach to edge detection, highlighting a practical quantum advantage in image processing.

quant-pharXiv:2604.23535•Apr 28, 2026•0 citations
This work introduces a novel quantum algorithm for gradient-based edge detection that operates entirely within the quantum circuit model. Grayscale images are encoded using the Novel Enhanced Quantum Representation (NEQR), allowing exact arithmetic on pixel intensities. Directional gradients are computed by generating superpositions of neighboring pixels via cyclic shift operations and performing subtraction with an exact quantum arithmetic circuit. To refine accuracy, we introduce a direction-aware shifting mechanism that aligns edges with the darker side of intensity transitions. Our novel Quantum Partitioning Algorithm enables efficient in-place thresholding of edge candidates. This work exhibits polynomial-time improvements and optimizes the ancilla count compared to previous NEQR-based quantum edge detection algorithms. These results demonstrate a resource-efficient and fully quantum approach to edge detection, highlighting a practical quantum advantage in image processing.