Recently, a research team from the Rudolf Technology Center in Slovenia proposed a new method to optimize the sparse subgraph problem, which has wide applications in fields such as network analysis ...

The constant scaling of AI applications and other digital technologies across industries is beginning to tax the energy grid ...

Sparse matrices are fundamental to many computational problems, and their efficient representation in quantum computing is crucial for unlocking the potential of quantum algorithms. The core ...

Abstract: Combinatorial optimization is a promising area for achieving quantum speedup. The quantum approximate optimization algorithm (QAOA) is designed to search for low-energy states of the Ising ...

A line of engineering research seeks to develop computers that can tackle a class of challenges called combinatorial optimization problems. These are common in real-world applications such as ...

KnapFormer 🐙: online load balancer for Diffusion Transformer (DiT) training, balancing heterogeneous image/video workloads across GPUs to boost training efficiency.

Abstract: Graph-structured combinatorial problems in complex networks are prevalent in many domains, and are computationally demanding due to their complexity and non-linear nature. Traditional ...