The Machine-Learning Online Optimization Package is designed to automatically and rapidly optimize the parameters of a scientific experiment or computer controller system.

M-LOOP optimizing a BEC.

M-LOOP in control of an ultra-cold atom experiment. M-LOOP was able to find an optimal set of ramps to evaporatively cool a thermal gas and form a Bose-Einstein Condensate.

Using M-LOOP is simple, once the parameters of your experiment are computer controlled, all you need to do is determine a cost function that quantifies the performance of an experiment after a single run. You can then hand over control of the experiment to M-LOOP which will attempt to find a global optimal set of parameters that minimize the cost function, by performing experiments and testing different parameters. M-LOOP uses machine learning to predict how the parameters of the experiment relate to the cost; it uses this model to pick the next best parameters to test to find an optimum as quickly as possible. This approach is known as Bayesian optimization.

M-LOOP not only finds an optimal set of parameters for the experiment it also provides a model of how the parameters are related to the costs which can be used to improve the experiment.

If you use M-LOOP please cite our publication where we first used the package to optimize the production of a Bose-Einstein Condensate:

Fast Machine-Learning Online Optimization of Ultra-Cold-Atom Experiments. Scientific Reports 6, 25890 (2016). DOI: Link 10.1038/srep25890


Quick Start

To get M-LOOP running follow the Installation instructions and Tutorials.