Exponentially improved detection and correction of errors in experimental systems using neural networks

TL;DR

This paper presents machine learning algorithms, including PCA and ANN, to efficiently model experimental systems, significantly reducing measurement requirements for optimization tasks such as ion trap electric field compensation.

Contribution

The paper introduces a novel combination of PCA and ANN for error detection and correction in experimental systems, applicable across various physics experiments.

Findings

Successful exponential reduction in measurements for ion trap electric field compensation

Effective use of PCA for systems with known models

ANN application for systems without prior models

Abstract

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.