Abstract
Amidst the rapid advancements in technology, there is a growing demand for processing an increasing volume and quality of images, which necessitates faster image processing capabilities. Enhancing the efficiency of image processing algorithms has thus become a critical priority. Existing quantum image edge detection algorithms tend to exhibit high circuit complexity, which is directly linked to the dimensions of the images being processed, leading to less than optimal computational velocities. In this study, we introduce a quantum image edge detection algorithm that is based on the Laplacian of Gaussian operator. This novel algorithm capitalizes on the quantum parallelism of quantum computing, resulting in a marked enhancement in both the speed and performance of edge detection. To substantiate the practicality of our approach, we conduct simulations using the International Business Machines Quantum (IBM Q) platform. The circuit complexity of our algorithm is meticulously computed, revealing a lower complexity compared to analogous quantum edge detection algorithms. Notably, this complexity is detached from the image size and is solely contingent upon the grayscale value range of the image pixels.