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This research presents a data-driven framework for autonomously aligning complex free-space optical systems, a critical requirement for scaling neutral-atom quantum processors. By using high-finesse optical cavities as probes, the method achieves rapid cold-start convergence within tens of seconds, addressing a major bottleneck in maintaining large-scale quantum architectures.
Data-driven structural diagnostics and autonomous alignment of complex optical systems
arXiv:2609.05984v1 Announce Type: new Abstract: The quest for autonomous alignment and maintenance of complex free-space optical systems is increasingly urgent for large-scale neutral-atom quantum processors. Leveraging a high-finesse optical cavity as a sensitive probe, we introduce a data-driven framework for structural diagnostics and closed-loop control that achieves cold-start convergence within tens of seconds. These results establish a rapid, scalable diagnostic-control methodology for autonomous alignment and continuous maintenance of complex free-space optical architectures.
Abstract:The quest for autonomous alignment and maintenance of complex free-space optical systems is increasingly urgent for large-scale neutral-atom quantum processors. Leveraging a high-finesse optical cavity as a sensitive probe, we introduce a data-driven framework for structural diagnostics and closed-loop control that achieves cold-start convergence within tens of seconds. These results establish a rapid, scalable diagnostic-control methodology for autonomous alignment and continuous maintenance of complex free-space optical architectures.
Quantum Physics (quant-ph); Optics (physics.optics)
arXiv:2609.05984 [quant-ph]
(or arXiv:2609.05984v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2609.05984 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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