The deep learning optimiser (DLO) was developed with one goal in mind: to optimise high dimensional physical systems. The ANU quantum optics group was working on optical quantum memories as a way to store quantum information carried by light. The platform of choice is something called a magneto-optic trap, a device capable of trapping and cooling atoms down to only a few hundred micro-kelvin! To get the best performance out of this device you need lots of atoms, something on the order of 10 billion.

The problem comes from controlling this device as it is very complicated. In fact, depending on how you define it there is a continuous space of possible control schemes, leading to an infinite dimensional problem! Clearly, this is too big. Instead, we decided the best thing to do would be to divide the space into small but manageable steps. Even taking relatively large steps we still ended up with 63 independent controls, too large for a human to stand there changing values.

This is where the DLO came in, a specialised tool designed specifically to operate with high dimensional and complex systems. The DLO works by using the power of artificial neural networks (ANNs) to provide an approximate representation of the landscape. In doing the DLO learns what controls work the best by continually predicting the best parameters and then subsequently testing them on the system. Applying this technique we were able to improve the setup to trap double the number of atoms in half the time

Since then, the DLO has been applied to a number of problems including optical nano-fibre traps, single electron transistors, single photon generation and a myriad of other complex problems. The original research paper is open access and published in Nature Communications.