Rapid and robust laser-frequency auto-locking using Bayesian-optimization and discrete-wavelet-transformation algorithms.

Authors

Min Jiang, Xiao-Li Chen, Si-Bin Lu, Jia-Hao Fu, Zhan-Wei Yao, Shao-Kang Li, Min Ke, San-Ming Song, Run-Bing Li, Jin Wang, Ming-Sheng Zhan

Published in

The Review of scientific instruments. Volume 97. Issue 9. Sep 01, 2026.

Abstract

Rapid and robust laser-frequency auto-locking is essential for the field deployment of quantum communications, quantum computing, and precision-measurement technologies; however, achieving this remains a considerable challenge. Here, we propose and demonstrate an auto-locking scheme employing Bayesian optimization and discrete biorthogonal wavelet transformation. First, the reference is rapidly sought by making intelligent use of historical observations, eliminating the inherent blindness of the traditional parameter-scanning method. Second, the frequency reference is robustly identified by pinpointing transition signals with the discrete biorthogonal wavelet transformation and analyzing their immutable frequency differences and relative magnitudes, which are determined by the inherent atomic structure. This proposed approach achieves a fivefold acceleration in reference searching compared to conventional scanning methods in the case where the laser frequency drifts far away from the reference. Crucially, it achieves an identification accuracy of more than 99.5%, even under severe 50% laser-intensity fluctuations, 9.95° photodiode misalignment, 18 °C Rb cell temperature elevation, and 0.9° misalignment between pump and probe beams. Finally, locking the laser frequency to the identified reference with a lead zirconate titanate-current double-servo loop narrows the linewidth to 20 kHz. We believe that this rapid, robust, and high-performance auto-locking technique will be pivotal toward the deployment of the next generation of practical quantum technologies in demanding field environments.

PMID:
42742325
Bibliographic data and abstract were imported from PubMed on 15 Sep 2026.

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