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Granular-Ball Quantum Clustering (GBQC) is proposed, a framework that tightly couples granular-ball structural abstraction with quantum feature learning, and significantly reduces quantum kernel evaluations and computational overhead, enabling quantum clustering experiments on larger datasets within parameterized quant...
An efficient and reliable weighted $K$-nearest neighbor classification framework based on quantum granular balls, termed QGB-W$k$NN is proposed, suggesting that reliability-aware quantum granular-ball learning provides a promising paradigm for efficient and robust nearest-neighbor classification.
Experimental results demonstrate that the proposed adaptive and efficient KNN approach via granular-ball computing outperforms existing KNN variants across multiple datasets in terms of both accuracy and efficiency.
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