Quantum Gradient-Based Approach for Edge and Corner Detection Using Sobel Kernels
2026-05-01 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors show how to do edge and corner detection in images using quantum computing, recreating popular methods called Sobel and Harris detectors. They compare two ways to encode images into quantum states and find that one method (QPIE) works better in their tests. Their approach calculates edges by measuring changes in pixel intensity with a quantum circuit, but still relies on some classical steps to improve accuracy. The experiments were done in a noiseless simulated environment, so real quantum computers might face challenges. Overall, the authors demonstrate that these image processing techniques can be implemented on quantum systems, though not yet faster than classical methods.
Edge detectionCorner detectionQuantum computingSobel operatorHarris detectorQuantum image encodingFRQIQPIEGradient computationQuantum simulation
Authors
Mohammad Aamir Sohail, Gabriela Pinheiro, Yasemin Poyraz Kocak, Batuhan Hangun, Emre Camkerten, Simge Yigit, Hafize Asude Ertan
Abstract
Edge detection refers to identifying points in a digital image where intensity changes sharply, indicating object boundaries or structural features. Corners are locations where gray-level 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 used to encode the input data and are comparatively analyzed. The proposed approach introduces a quantum gradient computation scheme based on lag-2 differences, enabling the evaluation of gradient-like features in superposition. To improve detection quality and reduce false positives, a classical post-processing step is applied to candidate corner points identified by the quantum circuit. Results show that the proposed quantum circuits produce outputs consistent with classical Sobel and Harris operators. Furthermore, the QPIE-based configuration yields more stable and coherent results than FRQI, especially under limited measurement shots. While gradient computation can be performed efficiently at the circuit level, the overall cost remains dominated by state preparation, measurement, and classical post-processing. All experiments are conducted under noiseless simulation, and performance on NISQ hardware may be affected by noise and measurement limitations. Therefore, this work demonstrates a functional and scalable quantum realization of classical edge and corner detection methods rather than an end-to-end speedup.