mlsanityData to 7 Oct 2026

Lowering the implementation barrier of neutral-atom quantum computing with agentic workflows

arXiv 2607.25834 ↗First posted 28 Jul 2026quant-ph cond-mat.quant-gas cs.AIcited by 1

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

Quantum computers are moving from research laboratories to industrial machines accessible via the cloud and integrated into high-performance computing facilities. However, translating theoretical quantum protocols into hardware experiments remains a major bottleneck, requiring expertise across protocol design, compilation, simulation, and cloud execution. Here, we introduce an agentic workflow that automates this pipeline on neutral-atom quantum processors (here two Pasqal QPUs available on the cloud) while keeping the researcher in the loop for critical validation. In three case studies from many-body physics and optimization, the agent went from published paper or patent to a QPU campaign run overnight. In particular, human intervention was crucial to ensure scientific validity: the agent selected an inadequate observable in one experiment and constructed a plausible but incorrect hardware diagnosis in another, with both failures detected only through domain-expert review. Finally, we use a second agent to classify a corpus of 633 Rydberg-array arXiv papers and show that nearly half are implementable on present-day QPUs while identifying specific hardware upgrades needed for the rest. Together, these results demonstrate that agentic workflows provide a practical bridge between theoretical ideas and physical hardware, opening quantum experimentation to a much broader scientific community.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 1 of its 9 references that are in the map. First posted on arXiv 28 Jul 2026.

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1 of 9 references.

References · 1

  1. 2025Towards autonomous quantum physics research using LLM agents with access to intelligent toolscited by 7

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