Conventional quantum algorithms are not feasible for solving combinatorial optimization problems (COPs) with constraints in the operation time of quantum computers. To address this issue, researchers ...

In the lower part of the figure, it can be seen that L2O leverages on a set of training problem instances from the target optimization problem class to gain knowledge. This knowledge can help identify ...

Researchers demonstrated a quantum algorithmic speedup with the quantum approximate optimization algorithm, laying the groundwork for advancements in telecommunications, financial modeling, materials ...

Computer scientists often encounter problems relevant to real-life scenarios. For instance, "multiagent problems," a category characterized by multi-stage decision-making by multiple decision makers ...

Dr. James McCaffrey of Microsoft Research says that when quantum computing becomes generally available, evolutionary algorithms for training huge neural networks could become a very important and ...

Dr. James McCaffrey of Microsoft Research uses full code samples to detail an evolutionary algorithm technique that apparently hasn't been published before. The goal of a combinatorial optimization ...

Research and investment in artificial intelligence (AI) have rapidly expanded over the past decade. The International Data Corporation predicts that global spending on cognitive and AI systems will ...

When people program new deep learning AI models — those that can focus on the right features of data by themselves — the vast majority rely on optimization algorithms, or optimizers, to ensure the ...