Agentic quantum many-body physics

Our agentic AI system consists of two interacting loops: a scientist loop that decides what to investigate, and an engineer loop that keeps the experiment running.

Two concentric cycles. The outer scientist loop runs knowledge to hypothesis to experiments to conclusions and back, improving knowledge. The inner engineer loop runs error syndromes to diagnosis to action to apparatus, stabilising it. The two meet where the experiments run on the apparatus — Figure 2. The scientist loop and the engineer loop, and how they interact. The experiments the outer loop designs run on the apparatus the inner loop keeps stable.

The scientist loop

The scientist loop follows the logic of experimental research: ask a question, formulate a hypothesis, design and conduct an experiment, and use the results to decide what to investigate next.

Closing this loop requires both creativity and scientific judgment — especially the ability to identify which experiment would teach us something new. Improving its capabilities requires broad understanding and reasoning in quantum many-body physics.

While current LLMs can carry out parts of this process with appropriate context engineering, they do not yet consistently demonstrate the depth of insight that leading physicists bring to scientific discovery. We believe fine-tuning on experimental experience could help bridge this gap.

The engineer loop

The engineer loop makes such scientific exploration possible. It monitors the apparatus, diagnoses faults from their symptoms, chooses and applies a fix, and checks whether normal operation has been restored.

This requires detailed knowledge of the particular experiment — the practical experience that researchers often describe as intuition. We have already demonstrated this kind of autonomous troubleshooting on our working experiment through context engineering.

Previously: Neutral-atom arrays and AI, in both directions.