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 ...

Many graph-based algorithms in high performance computing (HPC) use approximate solutions due to having algorithms that are computationally expensive or serial in nature. Neural acceleration, i.e., ...

A sparse linear array, composed of a uniformly spaced core subarray and an extended sparse subarray, is synthesized using a harmony search (HS) and an exploratory harmony search (EHS) algorithms. The ...

Sparse methods are primarily valuable for systems in which the number of non-zero entries is substantially less than the overall size of the matrix. Such situations are common in physical systems, ...

SparseEA is a MATLAB-based evolutionary algorithm designed for solving multi-objective feature selection problems in classification tasks. It leverages advanced evolutionary strategies to enhance the ...

The effect of tapering is to create a sparse approximate linear system that can then be solved using sparse matrix algorithms. Monte Carlo simulations support the theoretical results. An application ...

Luis A. Escobar, William Q. Meeker, Jr, Algorithm AS 292: Fisher Information Matrix for the Extreme Value, Normal and Logistic Distributions and Censored Data, Journal of the Royal Statistical Society ...

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

Government procurement contracts can be complicated, with extensive risk analysis and compliance reviews. The traditional ...

In recent years, the rapid rise of large language models (LLMs) has drawn widespread attention due to their outstanding performance in language understanding and generation. These models have not only ...