Automating quantum computing laboratory experiments with an agent-based AI framework.

ABSTRACT: Fully automated self-driving laboratories promise high-throughput, large-scale scientific discovery by reducing repetitive labor. However, they require deep integration of laboratory knowledge, which is often unstructured, multimodal, and hard to incorporate into current AI systems. This paper introduces the "k-agents" framework, designed to support experimentalists in organizing laboratory knowledge and automating experiments with agents. The framework uses large-language-model-based agents to encapsulate laboratory knowledge, including available operations and methods for analyzing results. To automate experiments, execution agents break multistep procedures into agent-based state machines, interact with other agents to execute steps, and analyze results. These results drive state transi

SUBMITTER: Cao S 

PROVIDER: S-EPMC12546452 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature