Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Optics Express

Maximized atom number for a grating magneto-optical trap via machine-learning assisted parameter optimization

Sangwon Seo, Jae Hoon Lee, Sang-Bum Lee, Sang Eon Park, Meung Ho Seo, Jongcheol Park, Taeg Yong Kwon, Hyun-Gue Hong

Published Oct 11, 2021• 19 citations
Publisher

Abstract

We present a parameter set for obtaining the maximum number of atoms in a grating magneto-optical trap (gMOT) by employing a machine learning algorithm. In the multi-dimensional parameter space, which imposes a challenge for global optimization, the atom number is efficiently modeled via Bayesian optimization with the evaluation of the trap performance given by a Monte-Carlo simulation. Modeling gMOTs for six representative atomic species - 7Li, 23Na, 87Rb, 88Sr, 133Cs, 174Yb - allows us to discover that the optimal grating reflectivity is consistently higher than a simple estimation based on balanced optical molasses. Our algorithm also yields the optimal diffraction angle which is independent of the beam waist. The validity of the optimal parameter set for the case of 87Rb is experimentally verified using a set of grating chips with different reflectivities and diffraction angles.

DOI
10.1364/oe.437991

Related papers

Grating magneto-optical trap optimization and drift-mitigation based on Bayesian learning

Zhilong Yu, Lang Chen, Junyi Duan +3 more

The grating magneto-optical trap (GMOT) is a promising approach for miniaturizing cold-atom systems. We demonstrated a real-time parameter optimization and drift-mitigation method for a GMOT system based on Bayesian learning. In a remarkable short convergence period, optimal numbers of cold atoms reached up to 7.7 × 106, which was nearly four times better than with manual optimization. The parameters included not only physical parameters but also mechanical parameters that can control the critical optical alignment. The results experimentally demonstrate that our work can efficiently optimize multiple parameters for a GMOT system and for the atom-based systems that need fine control. The machine learner employed a dual layer Bayesian learning, which could suppress the cost function drift due to the instability of the experimental parameters and environmental factors of the GMOT. The proposed approach validates the feasibility of Bayesian optimization in multiparameter cold-atom systems and can be applied to rapidly determine optimal parameters and high stability of general cold atom-based physical systems.

Applied Physics Letters•Feb 5, 2024•7 citations

Autonomous Parameter Optimization of Grating Magneto-Optical Trap Using Hybrid Algorithms

Duona Xie, Wei Li, Xiaokuan Li +1 more

This work introduces the design and implement of grating magneto-optical trap (GMOT). The GMOT in the experience uses grating chips and planar coil chip to reduce size. We adopt a hybrid optimization strategy combining genetic algorithm (GA) and Bayesian optimization (BO) to adjust system parameters. The GA performs broad global exploration using a population size of 5, maximum iterations of 5, mutation factor of 0.5–1, and crossover rate of 0.7. BO uses a Matern 5/2 kernel to ensure rapid convergence in later stages. The autonomous optimization system takes the fluorescence intensity of atomic clusters in CCD cameras as the objective function.The system adjusts parameters including cooling laser power, frequency detuning, repump laser power, and magnetic field gradient. Experimental results show the capture of approximately 1.17×106cold atoms, and temperatures maintained at the millikelvin level.

•Oct 17, 2025•0 citations

Design and optimization of grating chip for compact magneto-optical trap

Jing Dong, Xiao‐Kuan Li, Wei Li

Compared with the conventional three-dimensional magneto-optical trap (3D MOT), the grating magneto-optical trap (GMOT) enables the cooling and trapping of atoms with only a single beam of light, thereby significantly reducing the overall volume of the device. Here, a grating chip with high diffraction efficiency is designed, and its parameters are optimized through simulations, which effectively suppresses the power of the zero-order diffracted light. Subsequently, the grating chip is fabricated and characterized, and using this chip, we successfully trap 1.7×106 atoms. The temperatures of the atomic cloud in the axial and radial directions are 5.6 mK and 17.0 mK, respectively. The successful implementation of the atom cooling and trapping experiment based on the diffraction grating provides important reference for the miniaturization of cold atom-based quantum sensors.

Applied Optics and Photonics China (AOPC)•Oct 28, 2025•0 citations

Dual atom (87Rb-133Cs) grating magneto-optical trap

Lei Xu, Muming Li, Zhilong Yu +5 more

This paper proposes a dual-color grating chip design method for simultaneously capturing dual atomic clouds (87Rb and 133Cs). By simulating key parameters such as the grating period, etching depth, duty cycle, coating material, and thickness, the optimal design parameters were determined to ensure efficient dual-wavelength diffraction and maximize the number of captured atoms. Experimental results demonstrate the simultaneous trapping of 1.6E8 87Rb atoms and 7.8E6 133Cs atoms, thereby offering an approach for multi-species cold atom systems. This dual-species grating magneto-optical trap (GMOT) system has potential applications in precision measurements such as cold atom clocks, quantum interferometers, and quantum electrometry.

arXiv:2412.14440•Dec 19, 2024•0 citations

Machine-learning-based automated loading of strontium isotopes into magneto-optical trap

Korak Biswas, Kushal Patel, S. Sagar Maurya +2 more

We implemented optimization techniques of machine learning (ML) to obtain the mutually exclusive sets of experimental parameters that maximize the number of strontium atoms of different isotopes (88Sr, 86Sr, and 87Sr) in a magneto-optical trap (MOT). Machine learning optimization techniques are significantly faster than conventional manual optimization. While optimizing the parameters, these algorithms efficiently tackle the problem of being confined in one of the local maxima in the parametric space. Thus, ML can be implemented to automate the loading of different isotopes into MOT to perform multiple experiments in a single setup.

AIP Advances•Jul 1, 2023•2 citations

Dual atom ( 87 Rb- 133 Cs) grating magneto-optical trap

Lei Xu, Muming Li, Zhilong Yu +5 more

This paper proposes a dual-color grating chip design method for simultaneously capturing dual atomic clouds ( 87 Rb and 133 Cs). By simulating key parameters such as the grating period, etching depth, duty cycle, coating material, and thickness, the optimal design parameters were determined ( T = 210 nm, r = 0.5, h 0 = 100 nm, d = 1150 nm) to ensure efficient dual-wavelength diffraction and maximize the number of captured atoms. Experimental results demonstrate the simultaneous trapping of 1.6 × 10 8 87 Rb atoms and 7.8 × 10 6 133 Cs atoms, thereby offering an approach for multi-species cold atom systems. This dual-species grating magneto-optical trap (GMOT) system has potential applications in precision measurements such as cold atom clocks, quantum interferometers, and quantum electrometry.

Optics Express•Jan 24, 2025•11 citations
We present a parameter set for obtaining the maximum number of atoms in a grating magneto-optical trap (gMOT) by employing a machine learning algorithm. In the multi-dimensional parameter space, which imposes a challenge for global optimization, the atom number is efficiently modeled via Bayesian optimization with the evaluation of the trap performance given by a Monte-Carlo simulation. Modeling gMOTs for six representative atomic species - 7Li, 23Na, 87Rb, 88Sr, 133Cs, 174Yb - allows us to discover that the optimal grating reflectivity is consistently higher than a simple estimation based on balanced optical molasses. Our algorithm also yields the optimal diffraction angle which is independent of the beam waist. The validity of the optimal parameter set for the case of 87Rb is experimentally verified using a set of grating chips with different reflectivities and diffraction angles.
The grating magneto-optical trap (GMOT) is a promising approach for miniaturizing cold-atom systems. We demonstrated a real-time parameter optimization and drift-mitigation method for a GMOT system based on Bayesian learning. In a remarkable short convergence period, optimal numbers of cold atoms reached up to 7.7 × 106, which was nearly four times better than with manual optimization. The parameters included not only physical parameters but also mechanical parameters that can control the critical optical alignment. The results experimentally demonstrate that our work can efficiently optimize multiple parameters for a GMOT system and for the atom-based systems that need fine control. The machine learner employed a dual layer Bayesian learning, which could suppress the cost function drift due to the instability of the experimental parameters and environmental factors of the GMOT. The proposed approach validates the feasibility of Bayesian optimization in multiparameter cold-atom systems and can be applied to rapidly determine optimal parameters and high stability of general cold atom-based physical systems.
This work introduces the design and implement of grating magneto-optical trap (GMOT). The GMOT in the experience uses grating chips and planar coil chip to reduce size. We adopt a hybrid optimization strategy combining genetic algorithm (GA) and Bayesian optimization (BO) to adjust system parameters. The GA performs broad global exploration using a population size of 5, maximum iterations of 5, mutation factor of 0.5–1, and crossover rate of 0.7. BO uses a Matern 5/2 kernel to ensure rapid convergence in later stages. The autonomous optimization system takes the fluorescence intensity of atomic clusters in CCD cameras as the objective function.The system adjusts parameters including cooling laser power, frequency detuning, repump laser power, and magnetic field gradient. Experimental results show the capture of approximately 1.17×106cold atoms, and temperatures maintained at the millikelvin level.
Compared with the conventional three-dimensional magneto-optical trap (3D MOT), the grating magneto-optical trap (GMOT) enables the cooling and trapping of atoms with only a single beam of light, thereby significantly reducing the overall volume of the device. Here, a grating chip with high diffraction efficiency is designed, and its parameters are optimized through simulations, which effectively suppresses the power of the zero-order diffracted light. Subsequently, the grating chip is fabricated and characterized, and using this chip, we successfully trap 1.7×106 atoms. The temperatures of the atomic cloud in the axial and radial directions are 5.6 mK and 17.0 mK, respectively. The successful implementation of the atom cooling and trapping experiment based on the diffraction grating provides important reference for the miniaturization of cold atom-based quantum sensors.
This paper proposes a dual-color grating chip design method for simultaneously capturing dual atomic clouds (87Rb and 133Cs). By simulating key parameters such as the grating period, etching depth, duty cycle, coating material, and thickness, the optimal design parameters were determined to ensure efficient dual-wavelength diffraction and maximize the number of captured atoms. Experimental results demonstrate the simultaneous trapping of 1.6E8 87Rb atoms and 7.8E6 133Cs atoms, thereby offering an approach for multi-species cold atom systems. This dual-species grating magneto-optical trap (GMOT) system has potential applications in precision measurements such as cold atom clocks, quantum interferometers, and quantum electrometry.
We implemented optimization techniques of machine learning (ML) to obtain the mutually exclusive sets of experimental parameters that maximize the number of strontium atoms of different isotopes (88Sr, 86Sr, and 87Sr) in a magneto-optical trap (MOT). Machine learning optimization techniques are significantly faster than conventional manual optimization. While optimizing the parameters, these algorithms efficiently tackle the problem of being confined in one of the local maxima in the parametric space. Thus, ML can be implemented to automate the loading of different isotopes into MOT to perform multiple experiments in a single setup.
This paper proposes a dual-color grating chip design method for simultaneously capturing dual atomic clouds ( 87 Rb and 133 Cs). By simulating key parameters such as the grating period, etching depth, duty cycle, coating material, and thickness, the optimal design parameters were determined ( T = 210 nm, r = 0.5, h 0 = 100 nm, d = 1150 nm) to ensure efficient dual-wavelength diffraction and maximize the number of captured atoms. Experimental results demonstrate the simultaneous trapping of 1.6 × 10 8 87 Rb atoms and 7.8 × 10 6 133 Cs atoms, thereby offering an approach for multi-species cold atom systems. This dual-species grating magneto-optical trap (GMOT) system has potential applications in precision measurements such as cold atom clocks, quantum interferometers, and quantum electrometry.