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.
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.
Researchers at the French quantum computing company PASQAL have unveiled an AI agentic workflow that takes a paper or a patent as input and automatically designs, validates, and executes experiments on a neutral-atom quantum computer. Across three case studies the agent carried campaigns on real quantum processing units (QPUs) from source document to overnight run, demonstrating outstanding engineering capability. It also showed critical limitations, selecting a physically inadequate observable in one case and misdiagnosing a software bug as a hardware fault in another. Both failures were caught only through review by a domain expert. A second agent screened 633 related papers and found that among the 526 it could classify, 258 (49.0 percent) are compatible with present-day hardware, a result the authors use to argue that the bottleneck in quantum research has shifted from engineering to scientific judgment. [Quantum Biology Society] Quantum computers have moved beyond isolated laboratory instruments to become part of cloud-accessible high-performance computing infrastructure. But having access to the hardware does not automatically translate into a meaningful experiment. Running a published theoretical protocol on an actual QPU means passing through a long chain of expertise spanning quantum physics, software engineering, numerical simulation, cloud computing, and hardware characterization. Because of this implementation barrier, physically viable proposals are routinely left unexplored, asleep in the literature. A paper posted to the preprint repository arXiv in July 2026 by a team at PASQAL describes an ambitious attempt to dismantle that barrier using AI agents built on large language models (LLMs). The researchers constructed an automated workflow running from a natural-language prompt all the way to execution on a real QPU, and applied it to problems in many-body physics and optimization. The real value of the work, however, does not lie in the success of the automation. Through the plausible-looking errors the AI committed along the way, the authors make a sharp point: final scientific judgment still belongs to the researcher in the loop. ■ The Agentic Workflow: From Paper to Hardware Control PASQAL's neutral-atom quantum computers excite individually controlled atoms into Rydberg states to realize an Ising-like Hamiltonian. An experimental protocol comes down to three control handles: the atomic register, the Rabi frequency, and the detuning of the driving laser. The AI workflow the researchers built, using Anthropic's Claude Code with the Opus 4.8 and Fable 5 models, sits on top of PASQAL's open-source Python library Pulser, its emulators, and its Cloud SDK. The agent is organized as six modular skills that extract an experimental specification from a paper or patent, compile it into a pulse sequence, validate it through emulation, and submit it to real hardware, namely the FC1 device in Canada and SA1 in Saudi Arabia. Everything is structured around a single file, experiment_spec.json, so that the researcher can inspect and revise the agent's work at any point. A safety gate is built in as well: no protocol reaches the actual device without first passing noiseless and noise-aware emulation. ■ Competence, and a Costly Case of Cognitive Narrowing The researchers tested the agent's performance through three case studies. The first was a reproduction of the Kibble-Zurek mechanism experiment reported in Nature in 2019. The team deliberately instructed the agent to reproduce a phase (Z3) that current hardware cannot reach. After analyzing the live device specification, the agent returned an accurate physical verdict, reasoning that reaching the target regime at the minimum atomic spacing would force the Rabi frequency down so far that neither one Rabi period nor the nearest-neighbour interaction time would fit inside the sequence limit, and it redirected the target to the Z2 phase. In under an hour, and after three researcher prompts, it had jobs submitted to a real QPU and was returning good data. Notably, though, the higher-level moves that improved the result, switching from an open chain to a ring register and extending the study to the second QPU, came from the researcher rather than the agent. The limits of the AI emerged in the second case, the realization of magnetic phases in a triangular Rydberg array. Its verdict on hardware feasibility was flawless, but in choosing observables the agent displayed what the authors call cognitive narrowing, retaining only the structure factor and the mean magnetisation out of the four observables detailed in the original paper. The mean magnetisation is not the one-body order parameter of that paper and cannot distinguish the particular ordering pattern from any other state at the same density, yet the pipeline ran smoothly and the data looked plausible. The flaw surfaced only after the domain expert examined the results directly. With the researcher stepping in to designate the staggered magnetisation as the appropriate observable, and after 43 exchanges in total, physically meaningful QPU data were finally obtained on registers of 49 and 100 atoms. The third case was the implementation of a graph-coloring algorithm taken from a PASQAL patent. Here the agent showed formidable software engineering ability, working out through iterative back-and-forth with the user a scheme in which the full register is compiled once and each round is submitted as a separate job inside a single open batch, eliminating re-queuing latency between rounds while holding just one QPU queue slot. But when constraint-violating shots began appearing at an unexpectedly high rate, the agent confidently attributed them to hardware physics, diagnosing substantial Rydberg-blockade leakage on the QPU. The true cause was a simple data-labelling bug: a mismatch between the ordering of the atomic coordinates and the ordering of the measured bitstrings. Without expert scrutiny, this too could have been written off as a hardware defect. ■ Screening 633 Papers: Roughly Half of the Literature Is Ready for Today's Hardware The final part of the paper takes a wider view. A separate, lighter agent screened arXiv for Rydberg-array theory papers and assessed, quantitatively, how far each could be realized on current hardware. The search identified 633 papers, of which 107 could not be reliably classified. Among the 526 that could, 258 (49.0 percent) were judged compatible with publicly available PASQAL devices; 225 of these describe protocols complete enough to translate directly into executable form, while 33 need modest adaptation such as specifying an observable or a parameter range. Of the 268 papers that are not currently implementable, roughly 85 percent fail on the absence of XY-type interactions (141 papers) or local addressing (87 papers). The analysis shows both that today's quantum hardware has reached a level capable of absorbing a substantial body of theoretical proposals, and where future hardware development is most in demand. ■ Significance and Outlook: The Bottleneck Has Shifted from Engineering to Scientific Judgment This paper is still a preprint and has not been peer reviewed, but its message is unambiguous. AI agents proved overwhelmingly capable at dismantling the engineering barriers of quantum computing experiments, coordinating device interfaces, compiling code, and validating through simulation. Paradoxically, though, as the engineering effort falls away the researcher's role becomes more important than ever. Choosing a physically correct observable, bringing to bear the tacit knowledge that never appears on the page, and critically assessing whether a measured signal genuinely supports a physical claim remain, in the authors' words, irreducibly human. The two failures documented here make that point painfully well. In sum, this work shows that AI can dramatically lower the barrier to entry for quantum computing experiments while reaffirming the value of the human in the loop as the arbiter of scientific truth. Papers that once sat as static text are beginning to turn into executable scientific artifacts, and a new paradigm for quantum research is opening up. #QuantumComputing #NeutralAtoms #RydbergArrays #AIAgents #AgenticWorkflow #AutomatedScience #HumanInTheLoop #QuantumSimulation #CombinatorialOptimization #PASQAL #arXivPreprint Original paper (arXiv preprint): https://arxiv.org/abs/2607.25834 Related coverage (Nature, 19 August 2026): https://www.nature.com/articles/d41586-026-02495-4
Progress in quantum hardware requires a growing volume of experiments across diverse devices, straining the capacity of human experts. Automation remains challenging because device differences and drift can render workflows developed on one processor unsuitable for another. Here we present an agent-assisted experimental strategy for conducting experiments across three-qubit nuclear magnetic resonance processors. Developed on one processor and applied sequentially to four others, the strategy uses an agent to assess experimental evidence, select subsequent measurements and adapt procedures, with operators providing instrument preparation and physical intervention. Using three-qubit Grover state preparation as a common task, each deployment combined device-specific calibration, control-pulse optimization and measurement. The experimental strategy was refined through sequential deployment, and all four processors ultimately produced outputs dominated by their respective marked states. The marked-state populations of the reconstructed dominant eigenstates ranged from 0.910 to 0.959, with relative deviations below 3.8% from the ideal value of 0.945. These results demonstrate that an agent-assisted experimental strategy can be reused and refined across processors to realize a common quantum task, providing a basis for scaling experimental practice beyond individual devices.
The use of artificial intelligence (AI) in physics research is increasingly common, yet few guidelines exist for its effective use. Starting with a principles-based approach, we outline a set of ten requirements for collaborative AI agents in experimental physics, spanning scientific norms, effective agent use, and domain expertise. We take a human-centered approach in which agents act as collaborators that amplify rather than replace the expertise of a human researcher (user) who directs the work and is responsible for its results. We then present a framework designed to enforce these requirements, detailing methods for improving off-the-shelf behavior of large language models (LLMs) with custom scaffolding and describing general-purpose scientific workflows and skills. In particular, we highlight the power of closed-loop campaigns where agents work iteratively toward a well-defined goal. We demonstrate the potential of this mode of research through six case studies in experimental physics, covering device design, hardware control, neural network optimization, physical model identification, and data analysis. All six case studies result in useful work products that add utility to our research, including a neural network architecture and solid-state quantum defect photodynamic model that substantially outperform our published work; a microwave waveguide design that extends a published design concept to a fabrication-ready device meeting our experimental constraints; a quantum control pulse sequence candidate that improves simulated performance by more than an order of magnitude; commissioning of an NMR spectrometer to $\sim$1~Hz frequency precision on live hardware; and finally a data-fitting pipeline that achieves, to our knowledge, the first reconstruction of the Ising microstate of an artificial spin-ice array from diffraction-limited magnetic imaging.
Quantum algorithms and quantum hardware are advancing towards a promising paradigm for scientific applications. However, translating domain-specific problems into executable hybrid quantum-classical workflows remains a significant barrier for application researchers due to the required expertise in quantum algorithms, nuances in quantum programming, and hardware-aware system integration. At the same time, AI and primarily LLM based agents are increasingly capable of interpreting natural-language intent, reasoning over complex workflows, and translating high-level objectives into executable code and building computational pipelines. In this work, we introduce SQD Agent, an LLM-based agentic framework that translates natural-language user intent into executable workflows for Quantum Chemistry applications where algorithms from the Sample-Based Quantum Diagonalization (SQD) family are used. By automating this translation, SQD Agent reduces the level of human expertise and configuration overhead required, thereby simplifying experimentation in hybrid quantum-classical settings for application researchers new to quantum. SQD Agent adopts a modular and extensible architecture that supports seamless integration of heterogeneous quantum backends, classical solvers, and workflow components, ensuring adaptability to rapidly evolving quantum ecosystems. The framework further incorporates interactive capabilities for on-demand profiling, bottleneck analysis, resource optimization, intelligent result caching, and convergence visualization. Key features include quantum chemistry experiments, error mitigation on real quantum hardware, together with analysis of candidate mitigation schemes in terms of their potential error-recovery behavior and computational budget, helping users understand their practical trade-offs and decide which strategies to explore in subsequent experiments.
Quantum hardware advances now make it practical to explore quantum approaches to scientific problems. Applied quantum computing research sits at the intersection of domain knowledge, algorithms, theoretical physics, device physics, and quantum and classical computational methods. The breadth of expertise required and the inherent project complexity challenge not only any individual researcher, but also generic AI systems. Here, we introduce the Quantum Agentic Operating System (QAOS), a structured agentic research system designed to address this challenge for quantum and computational R&D projects. QAOS is built around a nonlinear workflow graph of heterogeneous multi-agent nodes, with a controlled flow of context and computational resources. It decomposes a complex research project into sandboxed multi-agent tasks executed under constraints from the project graph, task-relevant methodologies, and necessary verification steps. We demonstrate QAOS on a quantum chemistry prototyping task, in which it is asked to set up a minimal model for a proton-transfer problem and to implement its solution using sample-based quantum diagonalization (SQD), together with classical reference methods such as FCI (exact diagonalization) and density functional theory (DFT). We compare the QAOS solution to those obtained with baseline agents (Claude Sonnet 5 with the Claude Code harness). In contrast to QAOS, the baseline agents silently ignore explicit instructions, and the runs show large variability.
The integration of large language models (LLMs) into scientific research is accelerating the realization of autonomous ``AI Scientists.'' While recent advancements have empowered AI to formulate hypotheses and design experiments, a critical gap remains in the execution of these tasks, particularly in the domain of quantum computing (QC). Executing quantum algorithms requires not only generating code but also managing complex computational resources such as QPUs and high-performance computing (HPC) clusters. In this paper, we propose an AI-driven framework specifically designed to bridge this execution gap through the implementation of a Model Context Protocol (MCP) server. Our system enables an LLM agent to process natural language prompts submitted as part of a job, autonomously executing quantum computing workflows by invoking our tools via the MCP. We demonstrate the framework's capability by performing essential quantum algorithmic primitives, including sampling and computation of expectation values. Key technical contributions include the development of an MCP server for quantum execution, a pipeline for interpreting OpenQASM code, an automated workflow with CUDA-Q for the ABCI-Q hybrid platform, and an asynchronous execution pipeline for remote quantum hardware using the Quantinuum emulator via CUDA-Q. This work validates that AI agents can effectively abstract the complexities of hardware interaction through an MCP-based architecture, thereby facilitating the automation of practical quantum research.