Machine learning implemented for quantum optics by Skoltech scientists

IMAGE: The theoretical beam is the goal scientists wished to achieve. 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 the Nature Partner Journal, 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. Skoltech PhD student Adriano Macarone Palmieri, lead author of the study, described the findings as " a new open door towards deeper insights ." Adriano has a Master's Degree in Physics from Bologna and joined Skoltech from Italy, where he worked as a data scientist.