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arXiv 1809.04679cond-mat.quant-gasphysics.comp-ph
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散斑无序超冷原子的监督机器学习

Supervised machine learning of ultracold atoms with speckle disorder

S. Pilati, P. Pieri

摘要:

我们分析了如何准确地监督机器学习技术可以预测的最低能级的一维非相互作用的超冷原子受相关的障碍,由于光学散斑场。具有不同数量的隐藏层和每层神经元的深度神经网络在散斑场的大量实例上进行训练,这些实例的能级已经通过高阶有限差分技术预防性地确定。利用散斑场的傅里叶分量作为特征向量来表示散斑场实例。对决定监督机器学习任务可能成功的细节进行了全面分析,即神经网络的深度和宽度,训练集的大小以及正则化参数的大小。它被发现,基态能量以前看不见的情况下,可以预测基本上任意的精度。第一和第二激发态能量也可以预测,尽管精度稍低,并使用更多层的隐藏神经元。

英文摘要:

We analyze how accurately supervised machine learning techniques can predict the lowest energy levels of one-dimensional noninteracting ultracold atoms subject to the correlated disorder due to an optical speckle field. Deep neural networks with different numbers of hidden layers and neurons per layer are trained on large sets of instances of the speckle field, whose energy levels have been preventively determined via a high-order finite difference technique. The Fourier components of the speckle field are used as feature vector to represent the speckle-field instances. A comprehensive analysis of the details that determine the possible success of supervised machine learning tasks, namely the depth and the width of the neural network, the size of the training set, and the magnitude of the regularization parameter, is presented. It is found that ground state energies of previously unseen instances can be predicted with essentially arbitrary accuracy. First and second excited state energies can be predicted too, albeit with slightly lower accuracy and using more layers of hidden neurons.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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