Machine learning implemented for quantum optics

As machine learning continues to surpass human performance in a growing number of tasks, scientists at Skoltech have applied deep learning to reconstruct quantum properties of optical systems. Through a collaboration between the quantum optics research laboratories at Moscow State University, led by Sergey Kulik, and members of Skoltech's Deep Quantum Laboratory of CPQM, led by Jacob Biamonte, the scientists have successfully applied machine learning to the state reconstruction problem. Their findings have been reported in npj Quantum Information, and are the first to show that machine learning can reconstruct quantum states from experimental data in the presence of noise and detector errors. The MSU team generated data with an experimental platform based on spatial states of photons to prepare and measure high-dimensional quantum states. Experimental errors in state preparation and measurements inevitably plague the results and the situation becomes worse with increasing dimensionality.