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The so-called Hamming distance measures the difference between two binary strings A and B. In simplified form, it measures the number of changes in A to get B. This type of distance is very useful in classical computing in applications such as error correction. It is also advantageous in quantum computing, being for example widely used in quantum machine learning. Since current quantum computers have limited resources, this type of distance is particularly attractive because it can be computed using fewer qubits and operations than other distances such as Euclidean or Manhattan distances. In this paper, two circuits for calculating Hamming distances using exclusively Clifford+T gates are presented. The aim of both circuits is to reduce the quantum cost and number of T gates needed to compute the Hamming distance. The T gate is more expensive than the other gates, so this reduction will have a significant impact on the total cost of the circuits. Furthermore, the proposed circuits are implemented using only Clifford+T gates. The circuits implemented exclusively with this group of gates are compatible with proven error detection and correction codes. [ABSTRACT FROM AUTHOR]

To improve the accuracy of terminal area meteorological scene pattern recognition, this study adopts a clustering model based on Stacked Denoising Autoencoder. Noise is added to the input layer, and a three-layer autoencoder is constructed for greedy layer-wise training. The reduced-dimensional features are used as inputs for clustering to achieve meteorological scene pattern recognition. The method is validated using one year of meteorological data from Tianjin Binhai International Airport. Traditional similarity distance measures such as Euclidean distance, Hamming distance, and Manhattan distance are used with both K-medoids and FCM clustering methods. The results show that the similarity measure based on SDAE performs the best in both K-medoids and FCM clustering, with a difference rate of 22.4%, 12%, 17.7%, and 24.8%, 10.7%, 11.8% compared to other similarity measures, respectively. It also has the shortest computation time, demonstrating that the SDAE-based measure and clustering achieve the best performance. Ultimately, eight meteorological scenes are identified with clear and distinct classifications. [ABSTRACT FROM AUTHOR]

Nowadays content-based image retrieval (CBIR) framework is drawing in consideration of numerous analysts because of far-reaching applications found in numerous territories. In this paper, a new CBIR methodology is proposed and adequacy of any CBIR framework relies upon the features extracted from a color picture. In this work, firstly find the region of interest of the image using Sobel and Canny method and later on output is applied on HSV color space, it is clear to human vision eye. For classification, neural network is used and categorized the data with class labels. The similarity distance is estimated between the query image and stored image with different similarity metrics like Manhattan distance, Euclidean distance, Chebyshev, Hamming distance and Jaccard distance. The experimental result is estimated on accuracy, precision. The experiment performed on two well-known databases i.e.: Corel-1k and Corel-5k dataset and new methodology proves the better accuracy results up to 87.33% and 68.93% respectively and improves the precision results also up to 86.36% and 68.47% respectively. In this paper, results are also extended up to 80%. [ABSTRACT FROM AUTHOR]