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: 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 project implements sparse data structures (1D sparse array and 2D sparse matrix) using doubly linked lists. Instead of storing large arrays and matrices full of zeros (wasting time and memory), ...

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 ...

Fused3S is a CUDA kernel library that accelerates sparse attention by fusing Sampled Dense-Dense Matrix Multiplication (SDDMM), Softmax, and Sparse Matrix Multiplication (SpMM) into a single optimized ...

1 Introduction: Consequences of Numerical Inaccuracy 1 -- 1.1 Importance of Understanding Computational Statistics 1 -- 1.2 Brief History: Duhem to the Twenty-First Century 3 -- 1.3 Motivating Example ...

Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 43, No. 3 (1994), pp. 533-540 (8 pages) This item is part of a JSTOR Collection. For ...

By mimicking the brain’s neurons and synapses, neuromorphic computing is enabling smarter, energy-efficient AI systems. A ...

Relying on years of experience in the digitalization of ancient architecture, Tianbao Nait Technology Team has innovatively ...