Abstract: The sparsity-regularized linear inverse problem has been widely used in many fields, such as remote sensing imaging, image processing and analysis, seismic deconvolution, compressed sensing, ...

This approach enables block encoding of any sparse matrix with a clear gate-level construction, significantly reducing circuit complexity and control overhead, bringing quantum algorithms closer to ...

Abstract: Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental operation in graph computing and analytics. However, the irregularity of real-world graphs poses significant challenges to ...