We implement an agentic AI workflow built around a large language model (LLM) agent for autonomous experiments with nitrogen-vacancy (NV) centers in diamond. NV centers are a widely used platform for quantum sensing, and the ability to control many measurements from a computer makes NV experiments a natural setting for autonomous workflows. We make two main contributions. First, we demonstrate an autonomous NV experiment workflow that combines persistent project records, quantitative calculation and data analysis tools, and deterministic experiment control. In one autonomous experiment, the agent selected a single NV center, calibrated its resonant frequency, measured \(T_2^\ast\) with Ramsey measurements, and added a Carr--Purcell--Meiboom--Gill (CPMG) measurement to check a weak feature that could be related to nearby \(^{13}\mathrm{C}\). Second, we introduce two offline benchmarks that evaluate the agent's reasoning separately from laboratory execution. We evaluated both benchmarks with GPT-5.4, GPT-5.5, and GPT-5.6 Sol. In the Ramsey checkpoint benchmark, greater reasoning effort generally improved recognition of a residual resonance calibration offset. By contrast, in the pulsed optically detected magnetic resonance (pODMR) data evaluation benchmark, pulse sequence information alone produced more false positive resonance judgments at higher reasoning effort. Requiring an expected signal calculation kept false positive rates low across all three models and reasoning settings. The results suggest a clear division of labor for autonomous experiments. The agent forms scientific hypotheses and uses quantitative tools to evaluate data, while deterministic code controls the hardware and enforces safety constraints.
We implement an agentic AI workflow built around a large language model (LLM) agent for autonomous experiments with nitrogen-vacancy (NV) centers in diamond. NV centers are a widely used platform for quantum sensing, and the ability to control many measurements from a computer makes NV experiments a natural setting for autonomous workflows. We make two main contributions. First, we demonstrate an autonomous NV experiment workflow that combines persistent project records, quantitative calculation and data analysis tools, and deterministic experiment control. In one autonomous experiment, the agent selected a single NV center, calibrated its resonant frequency, measured (T_2^\ast) with Ramsey measurements, and added a Carr--Purcell--Meiboom--Gill (CPMG) measurement to check a weak feature that could be related to nearby (^{13}\mathrm{C}). Second, we introduce two offline benchmarks that evaluate the agent's reasoning separately from laboratory execution. We evaluated both benchmarks with GPT-5.4, GPT-5.5, and GPT-5.6 Sol. In the Ramsey checkpoint benchmark, greater reasoning effort generally improved recognition of a residual resonance calibration offset. By contrast, in the pulsed optically detected magnetic resonance (pODMR) data evaluation benchmark, pulse sequence information alone produced more false positive resonance judgments at higher reasoning effort. Requiring an expected signal calculation kept false positive rates low across all three models and reasoning settings. The results suggest a clear division of labor for autonomous experiments. The agent forms scientific hypotheses and uses quantitative tools to evaluate data, while deterministic code controls the hardware and enforces safety constraints.
An autonomous research laboratory could conduct an entire study, from formulating questions to preparing manuscripts. Artificial intelligence (AI) offers one route towards this goal. Recent AI systems take on several research roles, but may draw conclusions that the evidence does not support. Here, we present an autonomous research laboratory in which agents based on large language models assess one another's work and can stop or redirect investigations. They can generate and reject research questions, require separate theoretical checks and conduct experiments designed to challenge their claims. We demonstrate these capabilities by investigating ensembles of quantum spins in diamond. The agents derived the minimum number of calibration settings needed to predict responses to a defined family of microwave pulse sequences. They checked the result computationally and designed experiments to distinguish competing predictions. An initial experiment agreed with the theoretical predictions, but repeated reference measurements showed that the signal was drifting during data collection. The agents therefore treated the result as inconclusive and revised the measurement protocol without relaxing the acceptance criteria. The revised experiment met those criteria and supported the theoretical predictions. This study demonstrates that collaborating AI agents can select research questions, derive theoretical results and test their predictions experimentally.
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.
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
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.
The design of high performing quantum circuits remains largely dependent on human expertise. We introduce an autonomous agentic framework that employs large language models (LLMs) to conduct iterative quantum circuit designs under explicit design constraints. Our system integrates seven components: Exploration, Generation, Discussion, Validation, Storage, Evaluation, and Review. These components form a closed-loop workflow that combines web-based knowledge acquisition, literature-grounded critique, executable code generation, and experimental feedback. We evaluate the framework on two tasks: quantum feature map construction for quantum machine learning and ansatz generation for variational quantum eigensolver applications in quantum chemistry. In image classification benchmarks, the best generated feature map outperforms representative quantum feature maps and, when scaled to larger qubit counts, surpasses the classical radial basis function kernel. In molecular ground state estimation across seven molecules, the generated ansatz attains competitive accuracy with widely used chemically inspired and hardware-efficient constructions while satisfying the imposed scaling constraints. These results establish LLM driven agentic system as a viable paradigm for automated quantum circuit design and illustrate how AI systems can participate in iterative scientific optimization workflows across scientific domains.
Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optimization and reliable execution on noisy intermediate-scale quantum (NISQ) devices, yet it remains challenging due to the need for domain-specific planning, iterative code synthesis, and low-level calibration. In this paper, we present QAgent, the first autonomous multi-agent framework for end-to-end OpenQASM code generation. QAgent integrates schema-aware task planning, example- and tool-driven code synthesis, and hardware-aware calibration within a unified planning-synthesis-calibration workflow. The system leverages retrieval-augmented generation (RAG) to access structured kernel knowledge, examples, and backend constraints, and employs coordinated multi-agent reasoning with iterative execution feedback to ensure correctness. We evaluate QAgent on 12 representative quantum kernels and their compositions across five large language models (LLMs). Results show that QAgent improves Pass@1 accuracy by 47-70% on single-kernel tasks and achieves over 88% accuracy on multi-kernel workflows for large models, substantially outperforming existing baselines. Furthermore, under realistic hardware frequency drift, QAgent maintains near-unit execution fidelity through automated calibration, whereas SDK-based LLM methods suffer significant degradation. These results demonstrate that integrating planning, synthesis, and calibration is critical for reliable quantum program generation. The implementation of QAgent is open-sourced at https://github.com/fuzhenxiao/QAgent