Active Learning for Quantum Experimental Controling

Experimental control problems are popular task for experimental physicists in quantum exper- iments. Generally human tune the experimental parameters step by step, hand by hand to fine the suitable experimental parameters to set up the experimental system. While this way of tuning parameters is not efficient and may miss the ’global minimum’. Here we apply one semi-supervised machine learning method, active learning, to this parameters tuning task to find the suitable param- eters automatically. We will show the advantage of this machine learning method by two simulated examples. First we set the Efimovian expansion as a benchmark. Putting the unitary fermi gases into a harmonic trap with time dependent trapping frequency ω(t), active learning told us the most efficient way to release the trap is ω(t) ∝ 1/t which is assistant with the theory. Then we apply this active learning to evaporative cooling issue. Comparing to the simulated result, this active learning can give us a better cooling trajectory which can reach to a lower temperature during less time.