Quantum gradient-based approach for edge and corner detection using Sobel kernels
QUANTUM INFORMATION PROCESSING, vol.25, no.9, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 25 Issue: 9
- Publication Date: 2026
- Doi Number: 10.1007/s11128-026-05311-0
- Journal Name: QUANTUM INFORMATION PROCESSING
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Compendex, INSPEC, MathSciNet, zbMATH, Academic Search Ultimate (EBSCO), Technology Collection (ProQuest)
- Istanbul University Affiliated: No
Abstract
Edge detection refers to the process of identifying points in a digital image where the intensity changes sharply, typically indicating object boundaries or structural features. Corners are locations where the gray value intensity changes abruptly in multiple directions and are widely used in feature extraction, object tracking, and 3D modeling. In this study, we present a quantum implementation of Sobel-based edge detection and Harris-style corner detection. Two quantum image encoding methods: Flexible Representation of Quantum Images (FRQI) and Quantum Probability Image Encoding (QPIE), are employed to encode the input data, and their behavior is comparatively analyzed. The proposed approach introduces a quantum gradient computation scheme based on lag-2 differences, which enables the evaluation of gradient-like features in superposition. To improve detection quality and eliminate false positives, a classical post-processing step is applied to candidate corner points identified by the quantum circuit. The results demonstrate that the proposed quantum circuits produce edge and corner detection outputs that are qualitatively consistent with classical Sobel and Harris operators. Furthermore, the QPIE-based configuration yields more stable and structurally coherent results than its FRQI-based counterpart, while demonstrating greater robustness under limited measurement shots in the image reconstruction experiments. While the gradient computation can be performed efficiently at the circuit subroutine level, the overall computational cost remains dominated by state preparation, measurement, and classical post-processing steps. All experiments are conducted under noiseless simulation conditions, and practical performance on NISQ hardware may be affected by noise, measurement overhead, and finite-shot statistics. Therefore, the present work primarily demonstrates a functional and scalable quantum realization of classical edge and corner detection methods, rather than an end-to-end computational speedup.