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.

翻译：边缘检测是指识别数字图像中灰度值剧烈变化的点，这些点通常对应于物体边界或结构特征。角点是灰度强度在多方向上发生突变的位置，广泛应用于特征提取、目标跟踪和三维建模。本研究提出了基于Sobel算子的边缘检测和Harris角点检测的量子实现方案。我们采用两种量子图像编码方法——量子图像的柔性表示（FRQI）和量子概率图像编码（QPIE）——对输入数据进行编码并进行了对比分析。该方法提出了基于滞后二差分的量子梯度计算方案，实现了叠加态下梯度特征的评估。为提升检测质量并减少误检，我们对量子电路识别的候选角点施加了经典后处理步骤。结果表明，所提出的量子电路输出与经典Sobel和Harris算子结果一致。此外，基于QPIE的配置在有限测量次数下比FRQI产生了更稳定、更连贯的结果。尽管梯度计算可在电路层面高效执行，但总体成本仍主要受制于状态制备、测量和经典后处理环节。所有实验均在无噪声模拟环境下进行，在含噪中等规模量子（NISQ）硬件上的性能可能受噪声和测量限制的影响。因此，本研究展示了经典边缘与角点检测方法的功能性、可扩展量子实现方案，而非端到端的加速效果。