Agents for self-driving laboratories applied to quantum computing
This work addresses the problem of high-throughput scientific discovery for experimentalists by enabling automated, closed-loop control in laboratories, representing a novel method for a known bottleneck rather than a foundational breakthrough.
The paper tackles the challenge of automating scientific experiments by introducing the k-agents framework, which uses large language model-based agents to organize laboratory knowledge and automate multi-step procedures, demonstrating its application in calibrating a superconducting quantum processor to autonomously produce entangled quantum states at human-level performance for hours.
Fully automated self-driving laboratories are promising to enable high-throughput and large-scale scientific discovery by reducing repetitive labour. However, effective automation requires deep integration of laboratory knowledge, which is often unstructured, multimodal, and difficult 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. Our framework employs large language model-based agents to encapsulate laboratory knowledge including available laboratory operations and methods for analyzing experiment results. To automate experiments, we introduce execution agents that break multi-step experimental procedures into agent-based state machines, interact with other agents to execute each step and analyze the experiment results. The analyzed results are then utilized to drive state transitions, enabling closed-loop feedback control. To demonstrate its capabilities, we applied the agents to calibrate and operate a superconducting quantum processor, where they autonomously planned and executed experiments for hours, successfully producing and characterizing entangled quantum states at the level achieved by human scientists. Our knowledge-based agent system opens up new possibilities for managing laboratory knowledge and accelerating scientific discovery.