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The paper tests whether an AI agent can guide experimental workflows across several three-qubit NMR processors, with human operators handling physical setup. The agent is central to the method, but the results are about preparing Grover states on small hardware, with marked-state populations of 0.910 to 0.959 against an ideal of 0.945. For readers, the key point is the reusability claim: a strategy refined on one device transferred to four others, which is a modest but concrete step toward automating quantum experiments. Demonstrations this small do not yet show how far the approach scales.
Agent-assisted experimental strategy transfer across quantum processors
arXiv:2610.21047v1 Announce Type: new Abstract: 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.
Abstract: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.
arXiv:2610.21047 [quant-ph]
(or arXiv:2610.21047v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2610.21047 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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