Distributed algorithms for graph problems represent a vibrant area of study that addresses the challenges of decentralised computation across interconnected networks.

Distributed algorithms, on the other hand, furnish the mechanisms by which local interactions among agents lead to coherent and reliable global behaviour.

In this video from the PASC16 conference, Andrew Lumsdaine from Indiana University presents: Context Matters: Distributed Graph Algorithms and Runtime Systems. "The increasing complexity of the ...

DeepMind this week released Acme, an open source distributed framework for reinforcement learning algorithm development.

In a milestone that brings quantum computing tangibly closer to large-scale practical use, scientists at Oxford University Physics have demonstrated the first instance of distributed quantum ...

In particular, distributed stochastic gradient descent intensively invokes all-reduce operations for gradient update, which dominates communication time during iterative training epochs. In this work, ...

--MicroAlgo Inc.,, today announced the introduction of an innovative solution: a multi-simulator collaborative algorithm based on subgraph isomorphism, aimed at overcoming the limitations of qubit ...

We provide a market-based paradigm for decentralized problem solving and information processing that can be easily implemented to support real-time optimization of distributed systems. Management ...

The core concept of MicroAlgo's multi-simulator collaborative subgraph isomorphism algorithm is to decompose large quantum circuits into multiple smaller sub-circuits, leveraging parallel and ...