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Spectral clustering algorithm suffers from memory use and computational time bottleneck when handling large-scale image segmentation. By optimizing the selection of representative points before spectral embedding, a fast spectral clustering method with quantum immune optimization is proposed. The incorporation of quantum computing and immune clonal selection theory makes the selection of representative points more reasonable. The empirical study on the University of California Irvine standard data set clustering and synthetic aperture radar image segmentation demonstrates the efficiency of our algorithm and the capability to deal with large-scale data rapidly.