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.
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.
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.
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.
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.
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.
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.
The grating-based magneto-optical trap (GMOT) is a promising approach for miniaturizing cold-atom systems. However, the power consumption of a GMOT system dominates its feasibility in practical applications. In this study, we demonstrated a GMOT system based on planar elements that can operate with low power consumption. A high-diffraction-efficiency grating chip was used to cool atoms with a single incident beam. A planar coil chip was designed and fabricated with a low power consumption nested architecture. The grating and coil chips were adapted to a passive pump vacuum chamber, and up to 10 6 87 Rb atoms were trapped. These elements effectively reduce the power consumption of the GMOT and have great potential for applications in practical cold-atom-based devices.