Figure 4
(a) MLOOP deep learning network. Differential Evolution explores the input space (blue points) and the neural network creates a model of the data and predicts an optimum (red points). Maximum photon count of the neural network points is 96 ± 1% higher than manual optimization. Differential Evolution continues to explore the input space and has varied photon counts. The beginning point for the process (found by manually adjusting the 4 voltage set weights) was at 33700 counts/s and the highest photon count found by the neural network was at 66200 counts/s. (b) and (c) Fluorescence versus laser frequency detuning from the resonance for inital setting and after different optimizations. It can be seen from that the experimental values are very close to the theoretical Lorentzian fit29,30,31. This shows the heating is low before and after optimization and therefore the change in fluorescence can be used to infer the change in heating. Deviation from the theory near the resonance shown in (b) is a sign of small heating instability.
