Pixel identification in an image using Grover Search Algorithm

TL;DR

This paper explores using the Grover quantum search algorithm to identify specific pixels in images, demonstrating potential advantages of quantum computing for image processing tasks.

Contribution

It introduces a method to apply Grover's algorithm for pixel identification in images, showcasing quantum speed-up in image analysis.

Findings

Successfully identified black pixels in a 2x2 image using quantum algorithms

Demonstrated quadratic speed-up over classical search methods

Potential applications in steganography and image segmentation

Abstract

Quantum Computing offers an entirely new way of doing computation governed by the rules of quantum mechanics like Superposition and Entanglement. These rules allow us to do computation over all the possible states simultaneously. Hence, offering exponentially higher computation power than the present classical computers. Quantum computing algorithms are entirely different from classical algorithms due to quantum parallel computing derived from quantum state superposition and entanglement, which has natural advantages over classical image processing. Grover algorithm is a quantum-based search algorithm used to find the correct answer from an unsorted database by computing all the inputs simultaneously. Thus, giving us a quadratic speed-up of order O(n) 1/2 in comparison to the classical algorithm which offers speedup with order O(n). We used the Grover algorithm for identifying the black pixel in a (2x2) classical image by first converting it into a quantum state and then running the Grover algorithm for identifying the pixel with 0 value maximum gray-scale intensity. This technique has applications in areas like steganography offering data encryption between users, image segmentation.