Quantum Computing-Based Integration of Modulo 2n+1 Adders for Edge Detection

The rapid advancements in quantum computing have opened new possibilities for optimizing complex computational tasks, including image processing and edge detection. This Project explores the integration of Modulo 2 N +1 Adders in a quantum computing environment for efficient edge detection, utilizing key quantum gates such as the CNOT (Controlled NOT Gate) and Toffoli Gate (ControlledControlled NOT Gate). Traditional edge detection algorithms, such as Sobel and Canny, often suffer fromhigh computational demands, limiting their real-time or resource-constrained applications. By leveraging the inherent parallelism and superposition properties of quantum systems, the proposed approach integrates Modulo 2 N +1 Adders, which help reduce the computational complexity of arithmetic operations involved in edge detection tasks. The CNOT and Toffoli gates play a crucial role in the quantum circuit design, enabling the efficient implementation of modulo arithmetic and enhancing the speed and accuracy of edge detection. These quantum gates, which are fundamental in constructing quantum circuits, enable a significant reduction in the time complexity of detecting edges in images. The proposed system provides a promising solution for applications in computer vision, medical imaging, and real-time video processing. This study demonstrates how the integration of quantum gates and Modulo 2 N +1 adders can lead to the optimization of edge detection algorithms, offering scalable and efficient solutions that surpass classical computing methods.