论文标题
使用神经网络对实验系统中错误的检测和纠正呈指数改进
Exponentially improved detection and correction of errors in experimental systems using neural networks
论文作者
论文摘要
我们介绍了两种机器学习算法来创建实验设备的经验模型，该模型能够与无偏置的系统优化相比，将通用优化任务的测量次数减少。主成分分析（PCA）可用于降低描述数据的基本模型的情况下的自由度。我们进一步证明了人工神经网络（ANN）用于未知模型的任务。这使得提出的方法适用于涵盖实验物理多个领域的各种不同优化任务。我们在检测和补偿离子陷阱中的杂散电场的示例中证明了这两种算法，并以指数减少的数据量实现了成功的补偿。
We introduce the use of two machine learning algorithms to create an empirical model of an experimental apparatus, which is able to reduce the number of measurements necessary for generic optimisation tasks exponentially as compared to unbiased systematic optimisation. Principal Component Analysis (PCA) can be used to reduce the degrees of freedom in cases for which a rudimentary model describing the data exists. We further demonstrate the use of an Artificial Neural Network (ANN) for tasks where a model is not known. This makes the presented method applicable to a broad range of different optimisation tasks covering multiple fields of experimental physics. We demonstrate both algorithms at the example of detecting and compensating stray electric fields in an ion trap and achieve a successful compensation with an exponentially reduced amount of data.
