机器学习加速的玻色-爱因斯坦凝聚
Machine-learning-accelerated Bose-Einstein condensation
摘要：
机器学习正在成为一种可以增强物理实验执行和数据分析的技术。在这里，我们应用机器学习来加速$^{87}\mathrm{Rb}$原子的玻色-爱因斯坦凝聚体（BEC）的生产，通过贝叶斯优化多达55个控制参数。这种方法使我们能够在575 ms内从室温气体中制备$2.8 \times 10^3$光学捕获$^{87}\mathrm{Rb}$原子的BEC。该算法通过将高效的拉曼冷却应用于近量子简并，然后进行简短的最终蒸发来实现快速BEC制备。我们预计，许多其他复杂的非线性系统动力学的物理实验可以显着增强类似的机器学习方法。
英文摘要：
Machine learning is emerging as a technology that can enhance physics experiment execution and data analysis. Here, we apply machine learning to accelerate the production of a Bose-Einstein condensate (BEC) of $^{87}\mathrm{Rb}$ atoms by Bayesian optimization of up to 55 control parameters. This approach enables us to prepare BECs of $2.8 \times 10^3$ optically trapped $^{87}\mathrm{Rb}$ atoms from a room-temperature gas in 575 ms. The algorithm achieves the fast BEC preparation by applying highly efficient Raman cooling to near quantum degeneracy, followed by a brief final evaporation. We anticipate that many other physics experiments with complex nonlinear system dynamics can be significantly enhanced by a similar machine-learning approach.
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