基于Grover搜索算法的图像像素点识别
Pixel identification in an image using Grover Search Algorithm
摘要：
量子计算提供了一种全新的计算方式，它受叠加和纠缠等量子力学规则的支配。这些规则允许我们同时对所有可能的状态进行计算。因此，提供比目前经典计算机更高的计算能力。量子计算算法与经典算法完全不同，量子并行计算源于量子态的叠加和纠缠，相对于经典图像处理有着天然的优势。Grover算法是一种基于量子的搜索算法，用于通过同时计算所有输入来从未排序的数据库中找到正确答案。因此，给我们一个二次加速的顺序O（n）1/2相比，经典算法，提供加速的顺序O（n）。我们使用Grover算法来识别（2x2）经典图像中的黑色像素，首先将其转换为量子态，然后运行Grover算法来识别具有0值最大灰度强度的像素。该技术在诸如提供用户之间的数据加密的隐写术、图像分割等领域有应用。
英文摘要：
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.
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