The K-Means algorithms typically utilize Euclidean distance to calculate the similarity between data points when dealing with large-scale heterogeneous data. However， this method has problems of low efficiency and high computational complexity. Inspired by the significant advantage of Hamming distance in handling data similarity calculation， a Quantum K-Means Hamming （QKMH） algorithm was proposed to calculate similarity. First， the data was prepared and made into quantum state， and the quantum Hamming distance was used to calculate similarity between the points to be clustered and the K cluster centers. Then， the Grover’s minimum search algorithm was improved to find the cluster center closest to the points to be clustered. Finally， these steps were repeated until the designated number of iterations was reached or the clustering centers no longer changed. Based on the quantum simulation computing framework QisKit， the proposed algorithm was validated on the MNIST handwritten digit dataset and compared with various traditional and improved methods. Experimental results show that the F1 score of the QKMH algorithm is improved by 10 percentage points compared with that of the Manhattan distance-based quantum K-Means algorithm and by 4.6 percentage points compared with that of the latest optimized Euclidean distance-based quantum K-Means algorithm， and the time complexity of the QKMH algorithm is lower than those of the above comparison algorithms.

In Hematoxylin-Eosin （HE）-stained pathological images， the uneven distribution of cell staining and the diversity of various tissue morphologies bring great challenges to automated segmentation. Traditional convolutions cannot capture the correlation features between pixels in a large neighborhood， making it difficult to further improve the segmentation performance. Therefore， a Multi-Channel Segmentation Network with gated axial self-attention （MCSegNet） model was proposed to achieve accurate segmentation of nuclei in pathological images. In the proposed model， a dual-encoder and decoder structure was adopted， in which the axial self-attention encoding channel was used to capture global features， while the convolutional encoding channel based on residual structure was used to obtain local fine features. The feature representation was enhanced by feature fusion at the end of the encoding channel， providing a good information base for the decoder. And in the decoder， segmentation results were gradually generated by cascading multiple upsampling modules. In addition， the improved hybrid loss function was used to alleviate the common problem of sample imbalance in pathological images effectively. Experimental results on MoNuSeg2020 public dataset show that the improved segmentation method is 2.66 percentage points and 2.77 percentage points higher than U-Net in terms of F1-score and Intersection over Union （IoU） indicators， respectively， and effectively improves the pathological image segmentation effect and the reliability of clinical diagnosis.