New machine learning-assisted method rapidly classifies quantum sources

For quantum optical technologies to become more practical, there is a need for large-scale integration of quantum photonic circuits on chips. This integration calls for scaling up key building blocks of these circuits – sources of particles of light – produced by single quantum optical emitters. Purdue University engineers created a new machine learning-assisted method that could make quantum photonic circuit development more efficient by rapidly preselecting these solid-state quantum emitters. The work is published in the journal Advanced Quantum Technologies. Researchers around the world have been exploring different ways to fabricate identical quantum sources by "transplanting" nanostructures containing single quantum optical emitters into conventional photonic chips.