论文标题
使用加固学习在玻色网凝结中创建和操纵量化的涡旋
Creation and manipulation of quantized vortices in Bose-Einstein condensates using reinforcement learning
论文作者
论文摘要
我们将增强学习的技术应用于对非线性物质波的控制。在这种方法中，一种代理控制外部高斯电位的位置，强度和形状，以在谐波电位中捕获的玻色网凝结物（BEC）中创建和操纵量化的涡旋。 BEC的密度和速度分布在通过GROSS-PITAEVSKII进化获得的每一刻，直接输入到卷积神经网络中，以确定代理的下一个动作。我们证明可以在二维系统中产生固定的单涡流状态，并且可以在三维系统中产生固定的涡旋状态。
We apply the technique of reinforcement learning to the control of nonlinear matter waves. In this method, an agent controls the position, strength, and shape of an external Gaussian potential to create and manipulate quantized vortices in a Bose-Einstein condensate (BEC) trapped in a harmonic potential. The density and velocity distributions of the BEC at each moment obtained by the Gross-Pitaevskii evolution are directly input into a convolutional neural network to determine the next action of the agent. We demonstrate that a stationary single-vortex state can be produced in a two-dimensional system, and a stationary vortex-ring state can be produced in a three-dimensional system.
